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Radar MASINT
Radar MASINT
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Intelligence cycle management
Intelligence collection management
MASINT

Radar MASINT is a subdiscipline of measurement and signature intelligence (MASINT) and refers to intelligence gathering activities that bring together disparate elements that do not fit within the definitions of signals intelligence (SIGINT), imagery intelligence (IMINT), or human intelligence (HUMINT).

According to the United States Department of Defense, MASINT is technically derived intelligence (excluding traditional imagery IMINT and signals intelligence) that – when collected, processed, and analyzed by dedicated MASINT systems – results in intelligence that detects, tracks, identifies, or describes the distinctive characteristics target sources. in the US MASINT was recognized as a formal intelligence discipline in 1986.[1][2]

As with many branches of MASINT, specific techniques may overlap with the six major conceptual disciplines of MASINT defined by the Center for MASINT Studies and Research, which divides MASINT into electro-optical, nuclear, geophysical, radar, materials, and radiofrequency disciplines.[3]

Radar MASINT is complementary to SIGINT. While the ELINT subdiscipline of SIGINT analyzes the structure of radar directed on a target, radar MASINT is concerned with using specialized radar techniques that measure characteristics of targets.

Another MASINT subdiscipline, radiofrequency MASINT, considers the unintentional radiation emitted from a radar transmitter (e.g., sidelobes)

MASINT radar sensors may be on space, sea, air, and fixed or mobile platforms. Specialized MASINT radar techniques include line-of-sight (LOS), over-the-horizon, synthetic aperture radar (SAR), inverse synthetic aperture radar (ISAR) and multistatic. It involves the active or passive collection of energy reflected from a target or object by LOS, bistatic, or over-the-horizon radar systems. RADINT collection provides information on radar cross-sections, tracking, precise spatial measurements of components, motion and radar reflectance, and absorption characteristics for dynamic targets and objectives.

Radar MASINT can be active, with the MASINT platform both transmitting and receiving. In multistatic applications, there is physical separation among two or more receivers and transmitters. MASINT can also passively receive signals reflected from an enemy beam.

As with many intelligence disciplines, it can be a challenge to integrate the technologies into the active services, so they can be used by warfighters.[4] Still, radar has characteristics especially appropriate for MASINT. While there are radars (ISAR) that can produce images, radar pictures are generally not as sharp as those taken by optical sensors, but radar is largely independent of day or night, cloud or sun. Radar can penetrate many materials, such as wooden buildings. Improving the resolution of an imaging radar requires that the antenna size is many times that of the radar wavelength. Wavelength is inversely proportional to frequency, so increasing the radar frequency can improve resolution. It can be difficult to generate high power at the higher frequencies, or problems such as attenuation by water in the atmosphere limit performance. In general, for a fixed sensor, electro-optical sensors, in UV, visual, or infrared spectra, will outperform imaging radar.[5]

SAR and ISAR are means of combining multiple radar samples, taken over time, to create the effect of a much larger antenna, far larger than would physically be possible, for a given radar frequency. As SAR and ISAR develop better resolution, there can be an argument if they still are MASINT sensors, or if they create images sufficiently sharp that they properly are IMINT sensors. Radar can also merge with other sensors to give even more information, such as moving target indicator. Radar generally must acquire its images from an angle, which often means that it can look into the sides of buildings, producing a movie-like record over time, and being able to form three-dimensional views over time.

Line-of-sight radar MASINT

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Counterartillery radar

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See Counter-battery radar

Three US radar systems exist for detecting hostile artillery fire and backtracking to its source, serving the dual requirements of warning of incoming fires and counterattacking the firer. While they are intended to be used in three tiers against artillery of different ranges, there can be a problem of having a threat of an unexpected type fired into an area covered by the wrong tier. Proper site selection and preparation is necessary for all types.[6]

Proper planning includes avoiding clutter sources such as land surfaces, vegetation, buildings, complex terrain, aircraft (particularly rotary wing) and particulate matter kicked up by wind or aircraft. The enemy may attempt to avoid the directional radar systems or even use electronic countermeasures, so active patrolling, and activating the radar at random times and in random directions will act as a counter-countermeasure. Complementary acoustic and electro-optical systems can compensate for the lack of omnidirectional coverage by the AN/TPQ-36 and AN/TPQ-37.

To complement the counterartillery radars, additional MASINT sensors include acoustic and electro-optical systems.

A variety of ground-to-ground radars serve in counterbattery and surveillance roles, and also have some capability to detect helicopters. The LCMR, AN/TPQ-36, and AN/TPQ-37 radars are ideally used in a layered detection system, for short, medium, and long range detection. LCMR is omnidirectional, but the other two are directional and need cueing from omnidirectional sensors such as the combined electro-optical and acoustic Rocket Launch Spotter or a pure acoustic system such as HALO or UTAMS

AN/TPQ-36 and −37 counterartillery radars

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These 1980-vintage systems are not man-portable, and are directional, but they do have longer range than the LCMR.

Physically heavier than the LCMR, the AN/TPQ-36 Firefinder radar can detect cannon, rockets, and mortars within its range:

  • Artillery: 14,500 meters
  • Mortars: 18,000 meters
  • Rockets: 24,000 meters
AN/TPQ-36 medium-range Firefinder being positioned

It has a moving rather than omnidirectional antenna. Current improvements are intended to replace its old control computer with a laptop, enhance performance in high clutter environments, and increase the probability of detecting certain rockets.

First intended to provide a third tier against long-range threats, the AN/TPQ-37 Firefinder radar basic software filters out all other radar tracks with signatures of lesser-ranged threats. New software, required by the mortar threat in the Balkans, allows it to duplicate the Q-36 mortar detection range of 18 kilometers, while still detecting longer-range threats. Proper crew training should compensate for the reduced clutter rejection caused by accepting mortar signatures.

Long-range AN/TPQ-37

Standard TPQ-36/37 radars are semi-manual in their plotting. An Israeli enhancement makes the plotting fully digital .[7]

Ground surveillance radar

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Portable, and intended for tactical use, is the man-portable surveillance and target acquisition radar (MSTAR), originally developed for British use in artillery spotting, as the primary users of MSTAR, like its predecessor, were and are artillery observation parties, although it may be used for ground reconnaissance and surveillance. The MSTAR entered UK service in early 1991, slightly accelerated for use in the Gulf War. Its official UK designation is Radar, GS, No 22. MSTAR was developed and produced in UK in the mid 1980s by Thorn EMI Electronics (now part of Thales).

It is a Doppler radar operating in the J Band, and is capable of detecting, recognising and tracking helicopters, slow moving fixed-wing aircraft, tracked and wheeled vehicles and troops, as well as observing and adjusting the fall of shot. The US uses it used as AN/PPS-5B and −5C Ground Surveillance Radar (GSR) Sets, and Australia calls its version AMSTAR.

The GSR is a ground-to-ground surveillance radar set for use by units such as infantry and tank battalions. and BCT RSTA units. It can detect and locate moving personnel at ranges of 6 km and vehicles at ranges of 10 km, day or night under virtually all weather conditions. The radar has a maximum display range of 10,000 meters and the radar can alert the operator both aurally and visually.[8] The APS/PPS-15 is a lighter, shorter ranged version intended for airborne, light infantry, and special operations force use. These radars are more MASINT then general purpose radar, as the simpler ones have very little imaging power, but perhaps a light or sound indicating the direction and range of the threat.

Recognizing the threat of ground surveillance radar,[9] the Australian military is exploring personal radar warning receivers (RWR), approximately the size of a credit card, and intended principally for special operations forces who have to evade ground surveillance radar.

Fixed or semimobile ground installations

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The COBRA DANE ground station radar is an "AN/FPS-108, a phased array L-Band antenna containing 15,360 radiating elements occupying 95% of the roughly 100 by 100-foot (30 m) area of one face of the building housing the system. The antenna is oriented toward the west, monitoring the northern Pacific missile test areas.[10]"

Night view of the AN/FPS-108 Cobra Dane RADAR

Methods continue to evolve. COBRA JUDY was intended to gather information on long-range missiles, in a strategic role. One developmental system, COBRA GEMINI,[11] is intended to complement COBRA JUDY. It can be used for observing long-range missiles, but is also appropriate for theater-level weapons, which may be addressed in regional arms limitation agreements, such as the Missile Technology Control Regime (MCTR). Where COBRA JUDY is built into a ship, this dual frequency (S- and X-band) radar is transportable, capable of operating on ships or on land, and optimized for monitoring medium range ballistic missiles and antimissile systems. It is air-transportable to deal with sudden monitoring contingencies.

Ship-based

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Aft view of the USNS Observation Island showing the location of the AN/SPQ-11 Cobra Judy array.

The AN/SPQ-11 Cobra Judy radar, on USNS Observation Island (T-AGM-23), could also be guided by the COBRA BALL electro-optical sensors on an RC-135. Cobra Judy was supplemented by Cobra Gemini on USNS Invincible (T-AGM-24) starting around 2000 and was replaced by Cobra King in 2014 on USNS Howard O. Lorenzen (T-AGM-25).[12][13]

Active line-of-sight satellite radar

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The Soviet Union used a number of radar-equipped ocean reconnaissance satellites (RORSAT), which used strong radar systems, powered by an onboard nuclear reactor, to visualize vessels. These operated in the "pushbroom" manner, scanning a swath straight down.

US radar satellites, however, have emphasized SAR and ISAR.

Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) MASINT

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A synthetic aperture radar (SAR) system, exploits the fast movement of an aircraft or satellite, simulating a large antenna by combining samples over time. This simulation is called the synthetic aperture.[5]

Coupled with other MASINT and IMINT sensors, SAR can provide a high-resolution day and night collection capability. Recorded over time, it can be excellent for tracking changes. When operated at appropriate frequencies, it has ground- and water-penetrating capability, and is good for picking objects out of deliberate or natural clutter.

SAR is not, however, a trivial computational task. As the real antenna moves past the target, the range between target and antenna changes, which must be considered in synthesizing the aperture. In discussing SAR principles, Sandia National Laboratories also notes that, "for fine resolution systems, the range and azimuth processing is coupled (dependent on each other) which also greatly increases the computational processing".[5]

In spite of the difficulties, SAR has evolved to a size that can fit aboard a UAV. Flying on the MQ-1 Predator, the Northrop Grumman AN/ZPQ-1 Tactical Endurance Synthetic Aperture Radar (Tesar) started operations, in March 1996, over Bosnia. The AN/ZPQ-1 uses a radar signal in the 10 – 20 GHz J-band, and can work in strip map, spot map, and MTI modes. These modes are applicable to a wide range of MASINT sensors.

Strip map imaging observes terrain parallel to the flight path or along a specified ground path. Resolution depends on range and swath width, and can vary from 0.3 to 1.0 metres.[5]

Compare the two. The radar is not affected by night or weather.

Spot map mode covers 800 x 800 metres or 2400 × 2400 metres. In MTI mode, moving targets are overlaid on a digital map.

As well as large SAR aircraft such as the E-8 Joint Surveillance Target Attack Radar System (Joint STARS), whose AN/APY-3 radar has multiple modes including ground moving target indication, the US has highly classified radar satellites. Quill launched in 1964, was the first radar satellite, essentially a prototype. A system originally called Lacrosse (or Lacros), Indigo, and finally Onyx appears to be the only US radar satellite system, using pushbroom scans and "spotlighting" SAR.[14]

Given that the E-8 is a large aircraft that cannot defend itself, there are US attempts to move the E-8 capability into space, under a variety of names, most recently a simple "Space Radar". In an era of budget demands, however, this extremely costly new generation has not been launched.[14]

ISAR can produce actual images, but the discipline is generally called MASINT rather than IMINT. A much more modest ISAR capability is on the Navy's[15] SH-60 multimission helicopter, carried on destroyers, cruisers, and aircraft carriers. If budgets permit, the proposed E-8 aircraft, the replacement for the P-3 maritime surveillance aircraft, will carry ISAR.[16]

P-3 aircraft carry the AN/APS-137B(V)5 radar, which has SAR and ISAR capability. This is part of the general upgrading of the P-3 to make it a capable land surveillance platform.

The German Armed Forces' (Bundeswehr) military SAR-Lupe reconnaissance satellite system has been fully operational since 22 July 2008.

SAR interferometry

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This technique, first demonstrated in the 1970s from an army airborne system, has evolved considerably. At first, it estimated the angle-of-arrival of backscatter power from a pixel on the ground by comparing the phase difference of the backscattered wave as measured at two different locations. This information along with the traditional range and azimuth (Doppler) information allowed one to locate the imaged pixel in three-dimensions, and hence estimate the elevation of that pixel. Elevation-mapping interferometric SAR systems have since become an important remote sensing technology, with a very specific height-mapping mission. Interferometric SAR systems can now be obtained as commercial off-the-shelf (COTS) products.

Detection of mines, both on the active battlefield and in reconstituting nations with unexploded ordnance (UXO) remains a critical problem. As part of the Strategic Environmental Research and Development Program (SERDP), the U.S. Army Research Laboratory (ARL), starting in 1997, began an effort to collect, under extremely controlled condition, a library of UXO signatures.

UWB Synthetic Aperture Radar (SAR)

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As part of a larger research initiative to create technology that could detect targets buried or hidden by foliage, the U.S. Army Research Laboratory (ARL) developed multiple UWB SAR radar systems with promising object-penetration capabilities. These radar systems were fully polarimetric and were generally designed to be mounted on an all-terrain vehicle for mobile applications on the battlefield.  Examples of ARL-designed UWB SAR systems include the railSAR, the boomSAR, the SIRE radar, and the SAFIRE radar.[17][18]

The railSAR was among the earliest of the UWB SAR technology at ARL and was constructed as a stationary, rail-guided impulse radar system.[19] It was then incorporated into the development of the boomSAR in 1995, which emulated the functions of an airborne radar system.[20] Afterwards, the UWB SAR technology was eventually transferred onto a vehicle-based platform like with the SIRE radar and the SAFIRE radar for greater access and mobility.[17]

Steel crater test area

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Once the basic terrain signature is known, signatures are being collected from terrain that has been disturbed in a controlled manner. One such environment is at the Yuma Proving Grounds, a desert area where an existing Unexploded Ordnance (UXO) test site, the Steel Crater Test Area, has been used for a variety of sensor calibrations. It contains buried land mines, wires, pipes, vehicles, 55-gallon drums, storage containers and arms caches. For the Army studies to define the signatures of UXO detection, over 600 additional pieces of inert UXO were added to the Steel Crater Test Area, including bombs (250, 500, 750, 1000, and 2000 lb), mortars (60 and 81 mm), artillery shells (105 and 155 mm), 2.75-in. rockets, cluster submunitions (M42, BLU-63, M68, BLU-97, and M118), and mines (Gator, VS1.6, M12, PMN, and POM-Z).

Coherent change detection (CCD)

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In the 1990s, a new SAR application of coherent SAR showed the ability the detection and measurement of very small changes in the earth's surface. The simplest form of this technology, known as coherent change detection (CCD), had obvious military and intelligence applications, and is now a valuable tool for analysts. CCD complements other sensors: knowing that the surface changed may mean that analysts can direct ground-penetrating radar on it, measure thermal signatures to see if something is generating heat under the ground, etc.

Compare radar CCD and optical equivalents of the same subject. The CCD would not have been affected by night or weather.

Moving target indicator

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Moving target indications (MTI), at first, might seem just an adjunct to imaging radar, allowing the operator to focus on the moving target. That which makes them peculiarly MASINT, however is, especially in combination with other sensors and reference material, allows the measurement of a movement signature. For example, a tank and a truck both might be measured at 40 km/h when on a road. If both turn onto unpaved ground, however, the signature of the truck is that it might slow significantly, or demonstrate much lateral instability. The tracked vehicle, however, might exhibit a signature of not slowing when going off-pavement.

There are several electronic approaches to MTI. One is a refinement of CCD.[21] Differential interferometric SAR is even more precise than CCD. Its use in measuring the ground motion of earthquakes can complement seismic sensors for detecting concealed underground explosions, or the characteristics of those above ground.

Current research and development involves multiple coherent SAR collections to make even more sensitive measurements, with the capability to detect motion as small as 1 mm per year. The new techniques address many of the limiting factors associated with SAR interferometry, such as atmospheric induced distortions.[22]

UHF/VHF SAR

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UHF and VHF SAR have begun limited operations on Army RC-12 aircraft and may be implemented on the Global Hawk.[23] DARPA's WATCH-IT program developed robust low false alarm density change detection software to detect vehicles and smaller targets under foliage, under camouflage and in urban clutter, and developed tomographic (3D) imaging to detect and identify targets that have not relocated. VHF/UHF SAR for building penetration, urban mapping and performing change detection of objects inside buildings.

Terrain characterization technologies were also developed, including the abilities to rapidly generate bald-earth terrain height estimates and to classify terrain features from multipass VHF/UHF SAR imagery. In September 2004, DARPA demonstrated real-time onboard change detection (vehicles and IEDs) and rapid ground-station tomographic processing, as well as rapid generation of bald earth digital elevation models (DEMs) using stereo processing. In parallel, the Air Force Targets Under Trees (TUT) program enhanced the VHF SAR by adding a 10 km swath width VHF-only mode, developing a real-time VHF change detection capability/

Non-cooperative target recognition

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Driving research into Non-Cooperative Target Recognition (NCTR) is the fratricide problem, which, according to Army Maj. Bill McKean, is that "... our weapons can kill at a greater range than we can identify a target as friend or foe. Yet if you wait until you're close enough to be sure you are firing at an enemy, you've lost your advantage." The procedural approach of more restrictive rules of engagement (ROE), according to McKean, "What they found was, if you tighten the rules of engagement to the point that you reduce fratricide, the enemy begins inflicting greater casualties on you. "Waiting until you're sure in combat could mean becoming a casualty yourself.".[24] Technical approaches to fratricide prevention include:

  1. Systems that align with the weapon or weapon sight and are pointed at the intended target, and send an identification friend or foe (IFF) signal at it. If it responds correctly, it is treated as friendly, but otherwise unknown. Challenges here include the interrogation becoming a source of electronic targeting for the enemy, and trusting a response.
  2. "Don't shoot me" systems use a mesh of IFF interrogators that send challenges at a given position. Friendly forces identify in response, and the interrogators share the data. This may not work in terrain that may mask the challenge, response, or response sharing.
  3. Situational awareness systems rely on periodic updates of positional data to help users locate friendly forces, as long as the responses are timely and not masked by terrain
  4. Non-cooperative target recognition systems measure signature using acoustic and thermal radiation, radio emissions, radar techniques, etc. Comparing the measurements to classic MASINT signatures characterize the target.

Radar offers the potential of non-cooperative target recognition (NCTR). These techniques, which could work if IFF systems fail, have been especially secret. No one has yet proposed, however, NCTR that will be effective if a coalition partner is flying the same aircraft type as the enemy, as in Desert Storm. IFF, presumably with encryption, probably is the only answer to that problem.

One open-literature study combined several pieces of radar information: cross-section, range, and Doppler measurements.[25] A 1997 Defense Department report mentions "Air Force and Navy combat identification efforts focus on non-cooperative target recognition technologies, including inverse synthetic aperture radar imaging, jet engine modulation (JEM), and unintentional modulation on pulse-based specific emitters".[26]

NCTR on JEM specifically depends on the periodic rotation of the blades of a turbine, with variations caused by the geometry of the elements of the engine (e.g., multiple rotors, the cowling, exhaust, and stators). More generally, the idea of "micro-Doppler" mechanisms, from any mechanical movements in the target structure ("micro-motion dynamics"), extends the problem to cover more than rotating aircraft structures, but also automatic gait recognition of human beings.[27] The micro-Doppler idea is more general than those used in JEM alone to consider objects that have vibrational or other kinds of mechanical movement. The basics of JEM is described in .[28][29] One non-rotational effect would be the surface vibrations of a ground vehicle, caused by the engine, which would be different for gas turbines of tanks and diesel engines of trucks. ISAR is especially useful for NCTR, since it can provide a two-dimensional map of the micromovements.

Moving surfaces cause amplitude, Doppler frequency, and pulse modulation of the return. The amplitude modulation comes from moving surfaces of different reflectivity and angle of reflection. Doppler shifting of the returned signals is a function of the radar carrier frequency, as well as the speed of the radar source and target, with positive Doppler shift from surfaces moving toward the illuminator and negative shift of surfaces moving away from it. Moving surfaces impose a pulse width modulation.

Detecting modulation depends on the angle of the source versus the target; if the source is too far off-center with a turbine or other moving surface, the modulation may not be evident because the moving part of the engine is shielded by the engine mounting. Modulation increases, however, when the source is at right angles to the axis of rotation of the moving element of the target. For fully exposed moving elements, (e.g., propeller blades or helicopter rotors), modulation is a function of the radar beam being off-center to the center of the moving element.[29]

Multistatic radar MASINT

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The first radars used separate antennas for transmitting and receiving, until the development of the diplexer allowed the antenna to be shared, producing much more compact radar systems. Until the development of low-observability "stealth" technologies, compact antenna size was prized.

One of the first principles of stealth technology was to shape the surface of aircraft so that they did not reflect the transmitted beam directly back at the shared antenna. Another technique was to absorb some of the radar in the coating of the aircraft.

The more separate radar receiving antennas there are, the more likely it is that a reflection will go to a receiver distant from the transmitter. The graphic shows the terminology in bistatic radar, with a separate receiver and transmitter.

Bistatic radar theory

Passive covert radar

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Human activities generate a great deal of radio energy, as in communications, navigation, and entertainment applications. Some of these sources provide enough energy such that their reflection or transillumination can enable passive covert radar (PSR) MASINT, which is also called passive coherent location (PCL).

A foreign transmitter, preferably a purpose-built radar transmitter such as used in air traffic control, but really any powerful transmitted such as TV or FM, potentially can produce reflected signals that do not return to the designated receiver of the foreign radar operator. A signal may reflect such that it can be intercepted and fed into a friendly radar receiver, giving at least information on the presence of a radar target illuminated by the foreign transmitter. This is the simple case with the unintended reflection going to a single radar support receiver.

Interferometry is also possible with such systems.[30] This is especially attractive for naval vessels, which, since they often travel in groups, will have different times difference of arrival (TDOA) of the reflections from the foreign receiver. To restate an important difference, basic PCR works with a single radar receiver and conventional display format, from a single reflection. TDOA works with a set of reflections, from the same target, arriving at multiple points.[31] "Passive sensors are shown to make a valuable contribution to the air defence mission."

Another group evaluated the PCR technology in an environment like that of a naval task group[32] Ships have more space, and thus the equipment and power are less limited than for airborne or man-portable systems. This British study tested illumination with a Watchman air traffic control pulse doppler radar, and a Bridgemaster marine radar, against experimental receiver types. The researchers also developed simulations of the system.

Against the marine transmitter, the receiver combined a square-law: Power-level detector with cross-collation of a local copy of the pulse against the received signal. This method improved sensitivity for poorer time resolution, because correlated peaks are twice the width of uncorrelated peaks.

Using the air traffic control illuminator, the receiver used pulse compression filtering of a chirp signal, which provided processing gain along with the ability to separate closely spaced targets. This also implemented a moving target indicator that suppressed clutter, but it was recognized that an MTI signal would not be available in a noncooperative environment. They concluded their work demonstrated feasible convergence of PCR and TDOA, using a shipborne R-ESM system with communications among the receivers, such that the processed signal is an interferometric process.

References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Radar MASINT, also known as RADINT, is a subdiscipline of (MASINT) that involves the active or passive collection and analysis of electromagnetic energy reflected from targets using systems to detect, identify, track, and characterize objects based on their distinctive radar signatures. This intelligence discipline focuses on quantitative and qualitative measurements of -derived data, such as radar cross-sections, size, , , and motion characteristics, enabling the extraction of precise physical attributes beyond traditional . Radar MASINT employs various technologies, including line-of-sight radars for direct imaging, over-the-horizon (OTH) systems that use ionospheric reflection to extend detection ranges, and bistatic or multistatic configurations where transmitters and receivers are separated to enhance coverage and reduce vulnerability. Key applications include assessment, where it determines launch trajectories, configurations, and accuracy; verification, such as monitoring compliance with agreements through radar observations of test flights; and enhancing air and space by penetrating environmental obstacles like smoke, haze, or foliage with (SAR) techniques. Historically, radar MASINT has played a pivotal role in , with notable systems like the AN/FPS-17 phased-array radar in , which tracked Soviet missile tests during the , and the radar in , used to verify Strategic Arms Limitation Talks (SALT) compliance by analyzing signatures. Modern implementations integrate advanced and algorithms to handle complex data from high-frequency pulses, supporting autonomous target identification through embedded signature libraries managed by entities like the (NGIC). These capabilities underscore radar MASINT's importance in protecting forces from attacks, providing timely intelligence for decision-makers, and contributing to broader MASINT efforts across electromagnetic, acoustic, and nuclear domains.

Introduction to Radar MASINT

Definition and Scope

Radar MASINT, a subdiscipline of (MASINT), involves the collection, processing, and analysis of signals to derive precise measurements of target characteristics, signatures, and environmental effects. This includes parameters such as radar cross-section (RCS), Doppler shifts, and polarization, obtained through active or passive interception of electromagnetic energy reflected from targets. The scope of Radar MASINT encompasses both active and passive radar signals collected from diverse platforms, including ground, air, sea, and space-based systems. It emphasizes non-imaging and imaging modalities to support objectives like assessment, electronic development, and treaty verification, such as detecting launches or identifying weapon system performance. Unlike (SIGINT), which focuses on intercepting and exploiting communications content or electronic signals for informational value, Radar MASINT prioritizes the technical parameters and physical signatures of targets to characterize capabilities without relying on decoded messages. Examples of Radar MASINT applications include measuring the RCS of missiles to enable warhead discrimination during flight and identifying emitter parameters to map adversary radar deployments. As part of the broader MASINT framework, it integrates with disciplines like electro-optical but specializes in the (RF) spectrum for signature analysis. Radar MASINT was formalized within the U.S. Department of Defense (DoD) structure in the early , with the establishment of the Central MASINT Office under DoD Instruction 5105.58 to coordinate its management and policy.

Historical Development

The origins of Radar MASINT trace back to , when Allied and developed jamming and deception techniques to counter enemy detection systems, laying the groundwork for post-war efforts in emitter identification and electronic intelligence (ELINT). Early radar countermeasures, such as deployment and signal jamming, required intercepting and analyzing emissions to understand enemy characteristics, marking the initial steps toward measuring signatures for intelligence purposes. These wartime innovations evolved into structured ELINT programs, which focused on passive collection of signals to identify system types, frequencies, and operational parameters, distinguishing them from active use. During the 1950s era, the intensified focus on Soviet radar signatures through U-2 overflights equipped with ELINT sensors, enabling the collection of data on radar locations, capabilities, and emission patterns to support strategic . This period saw the transition from ad hoc countermeasures to systematic signature analysis, with U-2 missions from 1956 to 1960 providing critical insights into Soviet air defense s. The (DIA), established in 1961, began coordinating technical intelligence efforts that included to MASINT, though formal recognition of MASINT as an intelligence discipline occurred in 1986. In 1993, the DoD formalized MASINT management through the creation of the Central MASINT Office (CMO) under DIA, integrating DoD and intelligence community activities for disciplines like radar signature exploitation. Key milestones in the 1970s and beyond highlighted Radar MASINT's tactical applications, such as the development and deployment of the AN/TPQ-36 and AN/TPQ-37 Firefinder radars for counter-battery roles, achieving initial operational capability in the early 1980s to locate enemy artillery by analyzing projectile trajectories via radar measurements. By the 1990s, (SAR) systems advanced MASINT for monitoring ballistic missile activities, using space-based platforms to measure launch signatures and trajectories. Post-9/11, MASINT expanded significantly for counter-terrorism, integrating radar data with other intelligence to detect insurgent movements and improvised threats, marking a turning point in its operational maturity. Organizational evolution involved key U.S. entities like the National Security Agency (NSA) for ELINT processing, the National Reconnaissance Office (NRO) for space-based radar collection, and the U.S. Air Force's 480th ISR Wing for distributed common ground system integration of radar-derived intelligence. Internationally, the UK's Government Communications Headquarters (GCHQ) contributed through SIGINT partnerships that included radar emission analysis, supporting joint efforts in emitter identification. In the 2010s, NRO advancements in space-based SAR satellites enhanced global radar MASINT, providing persistent monitoring through cloud-penetrating imagery for signature analysis. Geospatial aspects of MASINT were further integrated into the National Geospatial-Intelligence Agency (NGA) framework around the early 2000s, aligning radar data with broader imagery intelligence.

Fundamental Principles

Radar Signal Analysis Techniques

Radar signal analysis in MASINT involves processing electromagnetic returns from targets to extract measurable parameters that reveal characteristics such as , , , and , often using active illumination or passive of emissions. Key techniques include time- analysis, which decomposes signals into time and components to identify (PRF) patterns and modulation types, enabling discrimination of emitters or target responses based on intra-pulse variations like agility or phase coding. This method is particularly useful for analyzing complex waveforms in cluttered environments, where spectrograms or transforms highlight non-stationary features. Radar cross-section (RCS) measurements form a cornerstone of analysis, quantifying a target's effective area in monostatic configurations (collocated transmitter and receiver) or bistatic setups (separated geometries), which provide additional angular diversity for signature refinement. For antennas, such as those in dishes or array elements, the RCS can be approximated by the equation σ=4πA2λ2\sigma = \frac{4\pi A^2}{\lambda^2}, where AA is the physical area and λ\lambda is the , reflecting resonant behavior under matched polarization conditions. Doppler processing complements RCS by profiling target velocity through frequency shifts in the return signal, using Fourier transforms or pulse-Doppler filters to resolve radial motion and separate moving objects from stationary clutter. Polarization signatures enhance discrimination by examining how targets alter the orientation of incident waves, with key channels including VV (vertical transmit/vertical receive), HH (horizontal/horizontal), VH (vertical/horizontal), and HV (horizontal/vertical), which reveal material properties and aspect-dependent . Emitter geolocation employs time-difference-of-arrival (TDOA) techniques, triangulating sources by measuring signal delays across multiple receivers, achieving accuracies on the order of kilometers depending on baseline separation and signal bandwidth. Clutter rejection is achieved via (CFAR) processors, which dynamically adapt detection thresholds based on local noise statistics to maintain a fixed probability of false alarms amid varying interference like sea clutter or weather returns. Signature libraries serve as reference databases of known RCS, Doppler, and polarization profiles for real-time comparison, facilitating target identification through correlation matching. Error sources, including (which causes signal via multiple reflection paths) and atmospheric (due to absorption by or ions), degrade parameter estimates and require through diversity techniques or modeling. Parametric models like the Swerling cases account for target RCS fluctuations over scans or pulses, with Swerling I and III representing slow chi-squared variations (e.g., for with multiple scatterers) and Swerling II and IV for fast fluctuations, improving detection probability predictions in environments. Integration with spectrum analysis extends to unintentional emissions, where broadband spectral examination of spurious radiation from target electronics or engines reveals unique fingerprints, aiding passive identification without direct illumination.

Collection Platforms and Sensors

Radar MASINT collection employs a diverse array of platforms to acquire electromagnetic data reflected from targets, enabling measurements of radar cross-sections, target dimensions, shapes, and through active or passive techniques. These platforms span ground-fixed installations for persistent monitoring, mobile ground and airborne systems for tactical flexibility, maritime vessels for oceanic operations, and space-based assets for global coverage. Sensors on these platforms typically include receivers to intercept signals across broad bands and phased-array antennas that facilitate electronic without mechanical movement, allowing rapid adaptation to dynamic threats. Ground-fixed platforms, such as over-the-horizon (OTH) radars, extend detection ranges beyond direct line-of-sight by leveraging ionospheric reflection, supporting strategic MASINT in missile warning and . A notable historical example is the U.S. Air Force's 440L OTH , deployed in the to collect signature data on potential adversaries during the . These systems often integrate bistatic configurations, where separate transmitter and receiver sites enhance covert collection by reducing self-emission risks. Mobile platforms provide agility in contested environments, with airborne variants like the RC-135V/W Rivet Joint aircraft serving as key assets for real-time radar signal interception. Equipped with ELINT pods covering 0.5-18 GHz, these pods capture emitter parameters and reflected energy to derive target signatures during theater operations. Maritime platforms, including ship-mounted arrays on vessels like the U.S. Navy's Aegis-equipped destroyers, use phased-array radars such as the to perform simultaneous surveillance and MASINT collection over vast ocean areas, fusing radar data with environmental signatures for enhanced threat assessment. Space-based platforms enable persistent, all-weather MASINT through satellites like the U.S. (Onyx) series, which employ (SAR) sensors to generate high-resolution images and motion data irrespective of or darkness. These systems orbit at low altitudes, providing global revisit capabilities for tracking mobile targets and infrastructure changes. Since the early 2000s, unmanned aerial vehicles (UAVs) have augmented traditional platforms, carrying compact radar payloads for extended loitering and low-signature surveillance in denied areas, as demonstrated in U.S. ISR operations. Operational deployments of these platforms often occur in high-threat zones, where challenges like low-probability-of-intercept (LPI) signals—characterized by low power, frequency agility, and wide bandwidths—complicate detection by passive receivers. To counter this, systems incorporate advanced for faint emission extraction. Sensor fusion with SIGINT integrates -derived metrics, such as Doppler shifts and angular data, with intercepted communications for comprehensive target characterization. Internationally, Russia's Kvant 1L222 Avtobaza ground-based system exemplifies ELINT-augmented MASINT, detecting and geolocating airborne emitters in the 8-18 GHz band to support space object tracking within the broader Space Surveillance Network.

Line-of-Sight Radar MASINT

Counterartillery and Weapon-Locating Radars

Counterartillery and weapon-locating radars represent a critical subset of Radar MASINT, focused on detecting, tracking, and characterizing incoming , mortar, and fire to enable rapid counterfire responses. These systems employ advanced to analyze radar echoes from projectiles in flight, determining key parameters such as launch point, , , and with high precision. By integrating these measurements, operators can classify munitions types—such as distinguishing short-range mortars from multiple launch systems (MLRS)—and predict sites to minimize casualties and direct retaliatory strikes. A primary application of these radars in Radar MASINT involves analysis to pinpoint the origin of , allowing forces to locate enemy firing positions within seconds. Velocity and measurements derived from Doppler shifts and angular tracking enable the differentiation of types based on ballistic profiles; for instance, the slower, lower- trajectories of mortars contrast with the high-velocity, steep arcs of MLRS rockets. This classification supports tactical decision-making, such as prioritizing threats from massed rocket barrages over sporadic . The AN/TPQ-36 and AN/TPQ-37 Firefinder radars, developed by the U.S. Army, exemplify key systems in this domain, utilizing 3D pencil-beam scanning to detect and track projectiles. The AN/TPQ-36 specializes in shorter-range threats like mortars and rockets, with ranges up to 24 km for rockets and 18 km for artillery, while the AN/TPQ-37 extends coverage to longer-range artillery up to 50 km, both employing phased-array antennas for rapid electronic steering and multi-target tracking. Upgrades in the , including enhanced software for low slow small (LSS) drone detection, have broadened their utility beyond traditional munitions to emerging aerial threats. Techniques in these systems include mortar trajectory mapping, which applies parabolic equations to model under gravity, using initial velocity vectors obtained from returns to compute apex height and descent path. Impact point prediction for counterfire relies on integrating these models with real-time data, achieving accuracies within 100 meters to guide responses. Such methods ensure that even partial trajectories from early flight phases yield reliable origin estimates. In operational contexts, Firefinder radars have been extensively used during U.S. operations in and from 2003 to 2021, significantly reducing indirect fire casualties by enabling preemptive countermeasures. A comparable Russian system, the (1L219) , offers similar 3D tracking capabilities up to 50 km for rockets, highlighting international parallels in MASINT-driven defense.

Ground Surveillance and Border Radars

Ground surveillance and radars in Radar MASINT are specialized systems designed for persistent monitoring of terrestrial movements across perimeters, enabling the detection and of intruders such as personnel or . These radars operate in challenging environments, including rugged and vegetated areas, to provide real-time alerts for security and tactical operations. By analyzing radar returns, they distinguish between human walkers and mechanized threats, enhancing without relying solely on visual or optical methods. A key application involves detecting personnel and vehicles crossing borders, where micro-Doppler signatures—arising from limb movements or wheel rotations—allow for of targets as walking humans versus wheeled or tracked . For instance, the oscillatory patterns from a person's produce distinct sidebands in the Doppler spectrum, differing from the smoother signatures of vehicle motion, enabling automated discrimination at ranges up to several kilometers. This technique supports non-cooperative target identification in low-visibility conditions, such as or , critical for securing extensive regions. Exemplary systems include the AN/PPS-5 series, a man-portable, battery-powered optimized for short-range ground surveillance, capable of locating moving personnel at up to 5 km and vehicles at 10 km under all conditions. Operating on Doppler shift principles for target isolation, it weighs approximately 22 kg, allowing units to deploy it rapidly for tactical perimeter defense. For border applications, Israel's EL/M-2105 tactical ground surveillance radar provides 360-degree coverage, detecting and tracking slow-moving ground targets like personnel at ranges exceeding 10 km, with adaptations for low-altitude clutter in perimeter monitoring. These systems emphasize mobility and low power consumption to integrate into forward-deployed networks. Core techniques in these radars include Ground Moving Target Indication (GMTI), which suppresses ground clutter by exploiting the Doppler frequency differences between stationary echoes (near zero Hz) and moving targets (typically 0.1-100 Hz for personnel). GMTI employs multi-pulse processing or space-time adaptive processing to filter out environmental returns from vegetation or soil, achieving detection probabilities above 90% in moderate clutter scenarios. Additionally, integration with seismic sensors enhances confirmation; unattended ground sensor networks combine alerts with vibration detection from buried geophones, reducing false alarms by correlating acoustic-seismic signatures with tracks for verified intruder events. This fusion is particularly effective in hybrid sensor arrays for border zones. Deployments of ground surveillance radars along the (DMZ) date back to the 1970s, where U.S. and South Korean forces utilized systems like the AN/PPS-5 and AN/TPS-25 to monitor cross-border incursions amid ongoing tensions. These installations provided early warning against infiltrations, contributing to the defense of the 38th parallel through continuous scanning of the 4 km-wide buffer. In the , adaptations for urban clutter have emerged in counter-insurgency contexts, incorporating low-frequency waveforms and AI-driven filtering to penetrate building multipath and dense foliage, as seen in operations integrating GMTI with urban terrain models for threat discrimination in populated areas.

Maritime and Ship-Based Radars

Maritime Radar MASINT plays a critical role in naval operations by characterizing surface and subsurface threats through the analysis of radar signatures from ship-based platforms. Vessel identification often relies on radar cross-section (RCS) measurements, which quantify the reflective properties of ships to distinguish between vessel types based on their geometric and material characteristics, such as the reduced RCS of stealth designs like the Swedish Visby-class . Wake analysis complements RCS by examining the turbulent patterns trailing vessels, enabling identification of ship size, speed, and type even in low-visibility conditions, as radar echoes from wakes provide persistent signatures for non-cooperative target recognition. Submarine periscope detection represents a key application of high-resolution in maritime Radar MASINT, where X-band radars achieve fine range resolution (e.g., 1 ft) to discriminate small, low-Doppler periscopes from sea clutter. Systems like the AN/APS-116, developed for carrier-based S-3 aircraft, employ fast scanning (300 rpm) and multiscan integration over 5 seconds to detect periscope exposures as short as 1-2 seconds, using signature discrimination based on range profiles and Doppler perturbations for reliable identification. Similarly, the AN/SPS-74(V) Periscope Detection Radar, deployed on Nimitz-class carriers, utilizes 480 MHz bandwidth and advanced to track small cross-section targets at tactically significant ranges, enhancing by separating periscope returns from clutter with low rates. Prominent ship-based systems for Radar MASINT include the Aegis , an S-band phased-array system capable of simultaneous multi-target tracking of over 100 air and surface threats at ranges exceeding 165 km for small targets. Operating from U.S. Navy cruisers and destroyers, the provides 360-degree coverage and supports signature data collection for threat assessment in blue-water and littoral environments, contributing to the Electronic Order of Battle by analyzing emissions from foreign fleets. Shipborne electronic intelligence (ELINT) systems further enable MASINT by intercepting and parameterizing signals from adversary vessels, such as Soviet-era shipborne radars during operations, to map foreign fleet capabilities and emission characteristics. Advanced techniques in maritime Radar MASINT address challenging environments through clutter modeling and bistatic configurations. clutter, dominated by Bragg from waves, is modeled using compound Gaussian distributions like the to predict amplitude statistics and Doppler spectra, facilitating small target detection by setting adaptive thresholds (e.g., via processors) that account for sea-state variations and polarization effects. Bistatic setups, incorporating passive receivers on buoys, enhance detection by exploiting forward-scatter geometries, where clutter statistics differ from monostatic cases—such as reduced at bistatic angles of 60°—allowing improved resolution of low-observable maritime targets like periscopes or small vessels. In operational contexts, ship-based MASINT has been integral to monitoring contested areas like the since the 2010s, where platforms such as China's Blue Ocean Information Network deploy semi-submersible radars for persistent vessel tracking and identification to assert maritime claims. These systems integrate radar data with for hybrid MASINT, fusing acoustic and electromagnetic signatures in multi-sensor networks to improve overall threat characterization and in naval .

Fixed and Satellite-Based Systems

Fixed and satellite-based systems provide persistent, wide-area coverage for Radar MASINT, enabling long-term of strategic activities without the mobility constraints of other platforms. These installations are particularly suited for monitoring fixed test sites, such as launch facilities, where radar signatures can reveal launch parameters, vehicle characteristics, and performance metrics over extended periods. For instance, ground-based radars detect and analyze radar cross-sections and trajectories during missile tests, contributing to assessments of range, accuracy, and payload configurations. In space surveillance applications, fixed radar networks catalog orbital objects by generating precise tracks that support identification, , and conjunction predictions. The U.S. Space Surveillance Network (SSN) integrates radar data from dedicated sensors to maintain a catalog of over 31,000 artificial objects as of 2024, including satellites and , with measurements refined through repeated observations to achieve accuracies on the order of meters in position and centimeters per second in velocity. Prominent fixed systems include the U.S. (AN/FPS-115), a phased-array radar operating in the UHF band (420-450 MHz) for early warning and space tracking, capable of detecting sea-launched threats at ranges exceeding 3,000 km while simultaneously cataloging satellites. Similarly, Russia's Voronezh-class radars, deployed across multiple sites in the , form a networked early-warning system using VHF/UHF frequencies to monitor launches and over vast areas, with nine operational stations enhancing continental coverage by 2024; however, as of 2025, some sites have faced disruptions due to conflicts, including strikes in 2024. Satellite-based systems extend Radar MASINT to global scales through active (SAR) payloads. Italy's constellation, comprising four X-band SAR satellites launched between 2007 and 2010, supports dual-use imaging for military , providing all-weather, day-night coverage with resolutions down to 1 meter in spotlight mode. Over-the-horizon (OTH) techniques in fixed ground systems, such as skywave propagation in the HF band (3-30 MHz), overcome line-of-sight limitations by refracting signals via the , enabling detection beyond 1,000 km for persistent monitoring. Revisit rates for satellite radars are governed by , including altitude, inclination, and constellation geometry; (LEO) systems like achieve average revisits of 12-24 hours globally, with targeted areas imaged more frequently through off-nadir pointing. The (NRO) has managed classified satellite MASINT programs since the 1990s, deploying radar imaging systems to collect signature data on ground targets and space objects, integrating with ground networks for comprehensive analysis.

Imaging Radar MASINT

Synthetic Aperture Radar (SAR) Fundamentals

(SAR) is a coherent imaging technique that leverages the forward motion of a sensor platform, such as an or , to synthesize a large virtual antenna aperture, enabling high-resolution two-dimensional images of or regardless of or illumination conditions. By collecting echoes over an extended path, SAR simulates the effect of a much longer physical antenna, improving azimuthal resolution far beyond that of conventional real-aperture systems. This motion-based synthesis allows for detailed mapping in (MASINT) contexts, where precise geometric and electromagnetic signatures are extracted from complex environments. The azimuthal resolution in SAR, denoted as δ\delta, is fundamentally determined by the synthetic aperture length LL, wavelength λ\lambda, and range RR to the target, following the relation δ=λR2L\delta = \frac{\lambda R}{2 L}. Here, LL represents the distance traveled by the platform during coherent integration of echoes, often spanning kilometers for spaceborne systems, which yields resolutions on the order of meters even at orbital altitudes. In MASINT applications, this high resolution facilitates terrain mapping to extract unique signatures, such as surface roughness or material properties, and enables detection of subtle changes like vehicle tracks or construction activities by comparing sequential images. Airborne SAR systems, exemplified by prototypes developed for the U-2 aircraft in the 1970s, marked early advancements in real-time reconnaissance, while spaceborne SAR proliferated post-2000 with missions like Germany's TerraSAR-X launched in 2007, offering global coverage and sub-meter resolution. Basic SAR image formation relies on the range-Doppler algorithm, which processes by first compressing pulses in the range direction using matched filtering to achieve fine range resolution, then exploiting Doppler shifts induced by platform motion to resolve positions through Fourier transform-based correlation. For non-broadside geometries, where the beam is angled forward or backward (squint mode), processing incorporates range cell migration corrections to account for varying range-Doppler histories, maintaining focus across the squinted beam. These techniques ensure that SAR-derived signatures in Radar MASINT provide actionable intelligence on target characteristics, supporting applications from border surveillance to infrastructure monitoring.

Inverse Synthetic Aperture Radar (ISAR) Applications

() exploits the rotational motion of targets relative to a stationary platform to generate high-resolution two-dimensional images, enabling detailed in operations. Unlike platform-motion-based , ISAR relies on the target's inherent dynamics, such as yaw, pitch, or roll, to synthesize an effective aperture for cross-range resolution. In (MASINT), ISAR provides non-cooperative imaging of moving platforms, supporting target identification without prior engagement. Key applications include ship imaging for structural identification and missile profiling for radar cross-section (RCS) characterization. For maritime targets, ISAR captures range-Doppler images of vessels at sea, leveraging wave-induced rotations to reveal hull contours, superstructures, and appendages, which aids in classifying ship types from standoff distances. In missile surveillance, ISAR images flying targets to map scattering centers and assess RCS variations due to rotating components like fins or warheads, enhancing threat evaluation during launch phases. These capabilities allow for real-time assessment of adversary assets, such as identifying naval vessels or ballistic trajectories. ISAR imaging employs range-Doppler to form 2D representations, where range resolution derives from waveforms like stepped-frequency or signals, and Doppler resolution emerges from the target's rotation over the coherent interval. Target is critical, using algorithms to correct translational and rotational instabilities; for instance, eigenspace-based methods or entropy-minimization techniques align envelopes and adjust phases, mitigating blur from non-uniform motion. These steps ensure focused images even for maneuvering targets, with cross-range resolutions as fine as centimeters at millimeter-wave frequencies. In MASINT, ISAR's value lies in feature extraction for non-cooperative recognition, isolating signatures from rotating parts like propellers on vessels, which produce distinct Doppler modulations indicative of types or operational states. Algorithms extract metrics such as scatterer positions, ship length, and strong reflector locations (e.g., masts or cranes) from multi-frame sequences, enabling automated classification against reference models with high accuracy in simulated and real scenarios. This supports tactical decision-making by distinguishing friendly from hostile assets without electronic . Historically, the U.S. Navy integrated via the APS-137 system on P-3C Orion aircraft in the mid-1980s, providing high-resolution vessel imaging during maritime patrols to support and surveillance. Recent advancements include UAV-mounted configurations for tactical , such as compact radars on medium-altitude platforms that deliver ship and profiles in contested environments, enhancing persistent monitoring with reduced to operators.

Interferometric and Change Detection Techniques

Interferometric synthetic aperture radar (InSAR) extends single-pass SAR imaging by exploiting phase differences between two or more SAR acquisitions to derive precise measurements of surface and deformation, enabling detailed MASINT assessments of terrain alterations or structural changes. In InSAR, the topographic phase component arises from the difference in range to the surface between acquisitions, which can be modeled as Δϕ=4πBhλRsinθ,\Delta \phi = \frac{4\pi B_\perp h}{\lambda R \sin \theta}, where Δϕ\Delta \phi is the interferometric phase difference, λ\lambda is the , BB_\perp is the perpendicular baseline between the positions, RR is the range to the target, θ\theta is the incidence , and hh is the variation. This technique allows for mapping with centimeter-level accuracy, supporting MASINT applications such as monitoring in urban or industrial areas, where gradual surface lowering can indicate underground tunneling or resource extraction activities. Coherent change detection (CCD) builds on InSAR principles by analyzing the phase coherence between repeat-pass SAR images to identify subtle environmental or man-made alterations, providing high-sensitivity MASINT for temporal monitoring. CCD computes the magnitude of the complex coefficient (coherence) between image pairs, where decorrelation in phase and signals disruptions to the structure at sub-resolution scales, often detecting motions as fine as sub-centimeter levels—such as tire tracks or minor earth disturbances—while preserving stable background features. In military contexts, this enables the discrimination of intentional changes, like adjustments or placements, from natural variations, enhancing signature intelligence on adversarial modifications. Ultra-wideband (UWB) SAR variants augment these techniques by operating across broad frequency spectra (e.g., 300–1000 MHz), facilitating foliage and ground penetration for concealed target detection in MASINT scenarios. Low-frequency UWB signals propagate through vegetation canopies and soil layers, revealing buried objects or subsurface anomalies; for instance, at the Steel Crater test site, UWB SAR imagery achieved resolutions of 5–6 inches to image underground facilities, including ventilation shafts and buried caches, through sub-banding optimizations that matched target resonances. Such capabilities support analysis of craters or disturbances indicative of , as seen in environmental and evaluations of hidden . Similarly, VHF/UHF SAR systems, exemplified by Russian designs like the , leverage long wavelengths (1–3 m) for enhanced penetration through foliage and ground clutter, aiding in the detection of low-observable or obscured assets in forested or urban environments. A notable application of CCD in operational MASINT occurred in the U.S. Army's Copperhead system during the 2000s, where UAV-mounted SAR compared image pairs to detect improvised explosive device (IED) emplacement through surface disruptions, achieving real-time alerts in adverse weather and deployed since 2009 following 2007 development. These methods collectively provide robust, all-weather tools for MASINT, prioritizing phase-based precision over amplitude alone to uncover otherwise invisible changes.

Target Recognition and Classification

Non-Cooperative Target Recognition Methods

Non-cooperative target recognition (NCTR) in radar measurement and signature intelligence (MASINT) involves identifying and classifying targets using inherent radar signatures without requiring the target to transmit cooperative signals, such as identification friend or foe (IFF) responses. This approach relies on analyzing the target's radar cross-section (RCS), Doppler characteristics, and high-resolution profiles to extract distinguishing features for discrimination in operational environments like air defense or surveillance. Key methods include template matching of RCS variations with respect to the target's aspect angle, which compares measured RCS patterns against pre-stored databases of known target signatures to achieve classification accuracy exceeding 90% under favorable aspect conditions. High-resolution range profiles (HRRP) provide one-dimensional (1D) signatures by resolving the target's structure along the radar line-of-sight, enabling feature extraction from echo amplitudes at sub-meter resolutions, as demonstrated in wideband radar systems for distinguishing aircraft types with recognition rates up to 95% in controlled tests. Applications of these methods span aerial and ground targets, leveraging micro-motion effects for enhanced discrimination. For aircraft classification, jet engine modulation (JEM) exploits the Doppler modulation induced by rotating engine blades, producing unique spectral signatures that allow identification of engine types and aircraft models, such as differentiating turbofan from turbojet configurations with modulation frequencies correlating to blade counts and rotational speeds. In ground vehicle recognition, wheel Doppler signatures arise from the micro-motions of rotating wheels, enabling type classification (e.g., wheeled versus tracked vehicles) through time-frequency analysis of radar returns, achieving detection accuracies around 85-90% in automotive radar experiments at ranges up to 100 meters. Algorithms for NCTR often employ feature-based recognition, focusing on scattering centers—dominant reflection points on the target that model the RCS as a sum of parametric contributions from geometric features like edges or corners. These features are extracted using techniques such as the adaptive Gaussian representation or CLEAN algorithms, providing aspect-invariant descriptors that improve robustness over raw profiles, though performance degrades with aspect changes exceeding 10-15 degrees due to profile misalignment. Aspect dependence remains a primary challenge, as signatures vary significantly with target orientation, necessitating alignment preprocessing or multi-aspect databases to maintain classification reliability above 80% across wide angular sectors. Efforts in the , including DARPA-funded programs on , advanced NCTR capabilities by integrating high-fidelity signature modeling for real-time processing in airborne radars. Furthermore, NCTR benefits from integration with electro-optical (EO) MASINT for multi-modal identification, where radar-derived features cue EO sensors for confirmatory imaging, enhancing overall accuracy in cluttered scenes by fusing RCS and visual signatures.

Moving Target Indication and Tracking

Moving Target Indication (MTI) and tracking techniques in MASINT enable the detection and continuous monitoring of dynamic objects by exploiting Doppler shifts induced by their motion relative to the radar platform, yielding measurable signatures of speed, direction, and acceleration for . These methods are essential in environments with high clutter, such as urban or vegetated areas, where stationary echoes can mask moving targets. In MASINT contexts, MTI data contributes to assessing threat dynamics, including personnel movement and trajectories, without relying on signals. A key technique is Space-Time Adaptive Processing (STAP), which suppresses clutter and interference in airborne systems to isolate ground moving targets for GMTI. STAP processes signals across multiple spatial channels and temporal samples, adaptively weighting them to null out stationary or slow-moving echoes while enhancing Doppler returns from targets. This approach has been integral to U.S. developments for collection, improving detection in radar ground moving target indicators by mitigating multipath and ground clutter. For instance, STAP enables persistent surveillance over large areas, supporting MASINT by providing velocity profiles that reveal target intent or formation. Micro-Doppler signatures extend MTI capabilities by capturing fine-scale vibrations and rotations superimposed on the target's primary Doppler shift, particularly useful for in human dismount tracking. These signatures arise from limb movements during walking, producing unique time-frequency patterns that distinguish individuals or activities even in low-resolution data. In applications, micro-Doppler facilitates remote identification of personnel in cluttered scenes, as demonstrated in research for battlefield detection where images integrated with micro-Doppler provide robust features unaffected by range or occlusion. This technique has been validated for recognizing human motions through spectrograms, enhancing MASINT by correlating motion patterns with behavioral signatures. In urban dismount tracking, MTI overcomes challenges like and building clutter by employing along-track or multi-aperture processing to estimate target velocities and positions. These systems track pedestrians or small groups in dense environments, providing real-time trajectories for tactical intelligence; for example, (SAR) images have been used to detect dismounts moving at speeds up to 5 m/s amid urban interference. Such applications are critical for urban operations, where offers all-weather, day-night persistence superior to optical sensors. Missile plume characterization leverages MTI to analyze the ionized exhaust trails of ballistic or cruise missiles, measuring plume expansion, velocity gradients, and cross-section (RCS) variations caused by plasma effects. returns from plumes exhibit distinct due to turbulent flow, allowing tracking of launch events and trajectory estimation even at high frequencies where occurs. Studies have shown that HF-band s can quantify plume impacts on RCS, for example increasing detectability by up to 24 dB at 10 MHz during boost phase, which informs MASINT assessments of missile types and performance. Feasibility analyses confirm 's utility for plume tracking, complementing spectral methods in non-cooperative scenarios. Integration of GMTI modes within SAR systems enhances MTI by combining high-resolution imaging with motion detection, where displaced phase centers or multi-channel processing isolates moving targets from stationary backgrounds. The Thales I-MASTER radar exemplifies this, operating in X-band to deliver simultaneous SAR maps and GMTI tracks for dismounts and vehicles over 800 km² per hour, supporting airborne MASINT platforms. Similarly, UHF and VHF radars excel in long-range foliage penetration for GMTI, using low-frequency wavelengths (0.3-1 m) to propagate through canopy with minimal attenuation, detecting concealed movers at ranges exceeding 20 km. The SRC FORESTER system, a UHF foliage penetration radar, provides wide-area GMTI in forested regions, enabling tracking of ground targets hidden from higher-frequency sensors. VHF-band SAR further supports this by forming coherent images and indicating movers via Doppler analysis. Israeli radar deployments in Gaza operations during the 2010s highlighted MTI advancements, with systems like RADA's MHR providing multi-mission hemispheric coverage for tracking rockets and dismounts in real-time amid urban clutter. In 2015 trials, these radars tracked multiple moving targets simultaneously, contributing to defensive during escalated conflicts. Post-2020, radar MASINT has addressed UAV swarm tracking challenges using frameworks like complex-valued to separate overlapping Doppler signatures from coordinated drones. This method achieves identification and quantification of swarm elements in dynamic scenarios, as validated in 2024 studies, enhancing counter-UAS capabilities by estimating individual velocities within groups of up to dozens.

Advanced and Multistatic Configurations

Multistatic and Bistatic Radar Systems

Bistatic radar systems represent a key configuration in Radar MASINT, where the transmitter and receiver are physically separated by a baseline distance, typically ranging from tens of meters to hundreds of kilometers, allowing for non-collocated geometries that exploit diverse angles. This setup enables the measurement of target signatures in regions not accessible to monostatic s, such as forward-scatter zones where the bistatic angle approaches 180 degrees. In these zones, the radar cross-section (RCS) of targets is amplified due to effects, providing enhanced signatures for even against stealthy platforms designed to minimize . The bistatic range, defined as the sum of transmitter-to-target and target-to-receiver distances, underpins in MASINT, linking directly to core signal analysis techniques for Doppler and range resolution. Multistatic radar networks build on bistatic principles by incorporating multiple transmitters and receivers, forming a distributed array that captures target data from various aspect angles simultaneously. These configurations facilitate 3D imaging through the fusion of bistatic measurements, yielding volumetric reconstructions with superior cross-range resolution and reduced sidelobe artifacts via techniques like frequency-domain . Coherent across nodes further enables the estimation of full 3D target velocity vectors, aiding in precise extraction for non-cooperative targets. A primary advantage of bistatic and multistatic systems lies in their reduced detectability, as receivers operate passively without emissions, evading enemy electronic warfare detection during MASINT collection. For low-observable targets, multi-angle illumination overcomes monostatic RCS reduction by leveraging forward-scatter enhancement, where RCS can increase by factors of 10 to 1000 depending on target size and , as governed by aperture-based models. Spatial diversity in multistatic nets also improves clutter rejection and tracking accuracy, with potential for lower transmitter power requirements due to optimized geometries. In applications, netted multistatic radars provide resilient tracking through redundant paths, enabling better discrimination of warheads from decoys via multi-perspective signatures. surveillance benefits from multistatic deployments using dedicated illuminators of opportunity, such as controlled broadcast signals, to achieve wide-area coverage for detecting low-altitude intruders with minimal infrastructure. U.S. DARPA-supported efforts in the advanced multistatic processing algorithms, as evidenced by real-time signal handling developments under early contracts that informed subsequent tests. European initiatives, including multistatic experiments for signature analysis, have validated micro-Doppler extraction in distributed setups, enhancing in complex environments.

Passive and Covert Radar MASINT

Passive and covert MASINT encompasses techniques that detect and track targets without emitting dedicated signals, thereby maintaining operational stealth and avoiding detection by adversaries. These methods rely on external illuminators of opportunity, such as commercial broadcast transmissions, to illuminate targets and capture reflected signals at receiver sites. This approach is particularly valuable in (MASINT) for gathering data on enemy movements without revealing the platform's location. A primary technique in passive radar is passive coherent location (PCL), which processes echoes from non-cooperative sources like FM radio or television broadcasts to determine target range, , and position. In PCL systems, the direct signal from the illuminator serves as a reference, while receivers capture bistatic echoes, enabling detection ranges up to 150 km for high-power FM stations under favorable conditions. Multistatic passive configurations extend this by deploying multiple receivers around a common illuminator or diverse sources, allowing emitter-free operation that enhances coverage and resilience against jamming. These techniques find critical applications in covert tracking, where passive systems monitor low-observable targets without alerting them through active emissions, and in urban surveillance, enabling discreet monitoring of ground vehicles or personnel in dense environments using ubiquitous signals. For instance, can track by exploiting multipath reflections from broadcast towers, providing real-time data for air defense without the radar cross-section vulnerabilities of traditional systems. In urban settings, it supports non-intrusive on or suspicious activities, leveraging the prevalence of commercial signals to avoid detection. Notable systems include the UK's CELLDAR, developed by Roke Manor Research and in the early 2000s, which uses reflections from cellular phone base stations to detect and track or vehicles at ranges suitable for tactical . Similarly, the U.S. Silent Sentry prototype, demonstrated by in the late 1990s, employed FM radio illuminators for passive coherent location, achieving real-time air over wide areas without dedicated transmitters. These systems highlighted the feasibility of low-cost, covert MASINT platforms. In the 2020s, has expanded to incorporate network signals as illuminators, offering higher bandwidth for improved resolution in target detection and tracking, with demonstrations showing viability for drone surveillance using synchronization signals. However, challenges persist in signal , as passive systems must align unknown illuminator references with received echoes amid direct-path interference and multipath clutter, often requiring advanced to mitigate ambiguities in range-Doppler maps.

Emerging Technologies and Challenges

Integration with AI and Machine Learning

Artificial intelligence (AI) and machine learning (ML) have transformed Radar Measurement and Signature Intelligence (MASINT) by automating complex data analysis tasks that traditionally required extensive human expertise. These technologies enable the processing of vast radar datasets to extract actionable insights, improving accuracy in signature identification and threat assessment in dynamic environments. Post-2020 advancements have focused on integrating AI for real-time decision-making, addressing the limitations of conventional signal processing in handling noisy or incomplete data. In applications such as automated signature classification, neural networks analyze radar returns to categorize targets based on unique electromagnetic signatures, enhancing non-cooperative target recognition (NCTR) without prior cooperation from the observed entity. For instance, convolutional neural networks (CNNs) process high-resolution range profiles (HRRPs) or (ISAR) images to classify aircraft or vehicles with high precision, even under varying aspect angles. in (SAR) imagery similarly leverages unsupervised neural networks to identify deviations from normal patterns, such as unexpected terrain changes or hidden structures, by learning baseline representations from unlabeled data. This approach has proven effective in detecting irregularities in SAR scenes, where traditional methods struggle with speckle noise and low contrast. Key techniques include CNNs tailored for NCTR, which extract hierarchical features from radar micro-Doppler signatures to distinguish between target classes like drones and birds. (RL) supports adaptive filtering by dynamically adjusting parameters, such as waveform selection, to optimize signal-to-noise ratios in cluttered scenarios; deep RL agents, for example, learn policies for scene-adaptive tracking that outperform static Kalman filters in multi-target environments. Real-time processing on edge devices further enables these methods by deploying lightweight AI models directly on hardware, reducing latency for applications and allowing without dependency. Post-2020 developments, such as the U.S. Defense Advanced Research Projects Agency's () Guaranteeing AI Robustness Against Deception (GARD) program, have advanced defenses against adversarial attacks on AI systems in applications. Integration with fusion techniques allows AI to combine inputs with multi-sensor streams, using for cross-modal alignment to improve overall accuracy in complex operations. The U.S. has incorporated AI-enhanced counter-unmanned aerial systems (counter-UAS) capabilities, incorporating ML algorithms into platforms for automated drone detection. Challenges in adversarial AI persist, particularly in spoofing detection, where attackers generate deceptive radar echoes to mislead classifiers; studies show that even robust CNN models can experience up to 30% accuracy drops under targeted perturbations, necessitating ongoing research into resilient architectures like ensemble methods. These vulnerabilities highlight the need for AI systems in Radar MASINT to incorporate explainability and continuous retraining to counter evolving threats.

Quantum and Low-Observable Detection Advances

Quantum radar represents a paradigm shift in Radar MASINT by leveraging quantum entanglement to enhance detection capabilities against stealth technologies, which minimize radar cross-section (RCS) through advanced materials and shaping. In entanglement-based systems, a pair of correlated photons is generated: one is transmitted toward the target as the probe signal, while the other is retained locally for correlation measurements, enabling the identification of weak returns that classical radars might dismiss as noise. This approach promises noise-resistant sensing, as quantum correlations preserve signal integrity even in high-clutter environments, potentially rendering low-observable (LO) targets like stealth aircraft detectable at longer ranges. Low-observable detection in Radar MASINT has advanced through the analysis of subtle signatures from RCS reduction countermeasures, including metamaterials that manipulate electromagnetic waves to absorb or redirect signals. These materials, often engineered with subwavelength structures, produce unique composite signatures—such as frequency-dependent patterns—that MASINT systems exploit for target , distinguishing LO platforms from natural clutter. Recent prototypes from 2023 to 2025 highlight practical progress, with initiating of single- detectors—a key enabler for entanglement-based —in 2025, specifically targeting like the F-22 and F-35 by piercing their reduced RCS through enhanced correlation. In , 21 member states signed the European Declaration on Quantum in 2024, committing to in quantum technologies, including sensing applications. Despite these advances, quantum radar faces significant challenges in scalability and decoherence, where environmental interactions disrupt photon entanglement, limiting operational range and reliability in real-world MASINT scenarios. Decoherence, exacerbated by thermal noise and propagation losses, requires cryogenic cooling and advanced quantum memories, hindering widespread deployment beyond prototypes. Ongoing research emphasizes hybrid classical-quantum architectures to mitigate these issues, ensuring robust LO detection for future Radar MASINT systems.

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