Principles of Modern Radar - Volume 2 1891121537

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Human Detection With Radar:Dismount DetectionCHAPTER16Sevgi Zübeyde Gürbüz✬Chapter Outline16.1 Introduction .................................................................. 70516.2 Characterizing the Human Radar Return ...................................... 71016.3 Spectrogram Analysis of Human Returns ..................................... 71916.4 Technical Challenges in Human Detection .................................... 72216.5 Exploiting Knowledge for Detection and Classification ........................ 72716.6 Summary..................................................................... 72916.7 Further Reading .............................................................. 72916.8 References ................................................................... 73016.9 Problems ..................................................................... 736✫✩✪16.1 INTRODUCTIONHuman detection, also known as pedestrian or dismount detection, has been accomplishedwith a variety of devices, including visual [1–5], acoustic [6,7], vibration/seismic [8–11],infrared [12–14], and laser [15] sensors in addition to radar. Many of these systems applysensor fusion techniques to combine the strengths of sensor modalities in a complementaryfashion. Examples include radar in conjunction with infrared sensors [14] or video cameras[16]. Although the augmentation of radar with other sensors may be useful in someapplications, the development of autonomous human detection capabilities for radar iscritical in many applications, especially those involving security and defense.Radar offers several unique advantages over other sensors, namely, the ability to operateremotely, far away from environments dangerous to operators, and in virtually allweather and lighting conditions. Through-the-wall radar can even provide the surveillanceof indoor environments from the outside of a structure. In contrast, visual, acoustic, infrared,and seismic sensors all require being placed in proximity to the target, without anyobstacles impeding the line of sight. Visual sensors can operate only during the daytime.Thus, radar remains the principal sensor of choice for many surveillance applicationsinvolving human detection.Over the years several platforms capable of performing human detection, in additionto detection of vehicles, have been developed. One of the older systems still in use by theU.S. Army is the AN/PPS-5 Ground Surveillance Radar, originally developed in 1966 bythe Eaton Corporation. It is a portable system operating in a band of 8.8–9 GHz, capable705
706 CHAPTER 16 Human Detection With Radar: Dismount DetectionTABLE 16-1Performance Specifications of Currently Available Ground Surveillance Radar PlatformsMinimumRangeDetectableSystem Band Maximum Range Accuracy VelocityAN/PPS-5 8.8–9 GHz Personnel: 5 kmVehicles: 10 kmAN/PPS-5C 10–20 GHz Single Man: 7 kmSmall Vehicle: 16 kmLarge Vehicle: 23 kmSR Hawk 16.21–16.50 GHz Single Man: 10 kmAvg. Vehicle: 30 kmSTS-700 26.5–40 GHz Personnel Walking: 700 mPersonnel Running: 700 mPersonnel Low Crawl: 500 mPersonnel Crawling: 700 mPersonnel Swimming: 500 mVehicles: 700 mSTS-1400 26.5–40 GHz Personnel Walking: 1400 mPersonnel Running: 1400 mPersonnel Low Crawl: 500 mPersonnel Crawling: 1400 mPersonnel Swimming: 500 mVehicles: 1400 mSTS-2800 26.5–40 GHz Personnel Walking: 1900 mPersonnel Running: 1900 mPersonnel Low Crawl: 500 mPersonnel Crawling: 1900 mPersonnel Swimming: 500 mVehicles: 2800 m20 m 1.6 km/h(0.45 m/s)8.3 m 1.25 m/s (radial)10 m 0.13–0.25 m/s(radial)Not GivenNot GivenNot GivenSTS-4400 Not Given 4400 m (person or vehicle) Personnel: 0.4 m30 mph Vehicle: 1.5 mSTS-12000 Not Given Personnel: 9.7 kmVehicles: 12 kmSources: [17–23].3m0.1 m/s0.1 m/s0.1 m/s0 m/s0m/sof detecting personnel at ranges up to 5 km and vehicles up to 10 km. This radar hasundergone several upgrades; the most recent (AN/PPS-5D) extends personnel detectionto 10 km and vehicle detection to 20 km [17–19]. A next-generation ground surveillanceradar developed by SRC, Inc., the SR Hawk, operates at a band of 16.21–16.50 GHz witha maximum detection range of 10 km for a single walking person and 30 km for a typicalvehicle [20]. The STS family of radars developed by ICX Technologies also offers similarperformance, with the STS-12000 Long-Range Perimeter Surveillance Radar offering amaximum range of 9.7 km for personnel and 12 km for vehicle detection [21–23]. Keyperformance parameters of currently available ground surveillance radars are summarizedin Table 16-1.These figures, however, represent the best possible performance of the radar, oftenunder near ideal conditions. In typical situations, human targets are easily masked byground clutter. This ground clutter is the result of strong reflections from the surrounding
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Principles of Modern Radar
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Published by SciTech Publishing, an
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Brief ContentsPreface xvPublisher A
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xContents3.6 Adaptive MIMO Radar 10
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xiiContents9.3 Adaptive Jammer Canc
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xivContents15.3 Multisensor Trackin
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xviPrefacehas always been the unlik
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Publisher AcknowledgmentsTechnical
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Editors and ContributorsVolume Edit
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xxiiEditors and ContributorsDr. Lis
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xxivEditors and ContributorsMr. Ara
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2 CHAPTER 1 Overview: Advanced Tech
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1.3 Radar and System Topologies 5FI
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1.4 Topics in Advanced Techniques 7
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1.6 References 15TABLE 1-1Summary o
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PART IWaveforms and SpectrumCHAPTER
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20 CHAPTER 2 Advanced Pulse Compres
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2.2 Stretch Processing 35is applied
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2.2 Stretch Processing 37TABLE 2-1
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2.5 Stepped Frequency Waveforms 61T
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2.5 Stepped Frequency Waveforms 63w
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2.5 Stepped Frequency Waveforms 69I
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2.9 References 812.7.4 SummaryWhile
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2.9 References 83[27] Taylor, Jr.,
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2.10 Problems 85shift across the pu
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88 CHAPTER 3 Optimal and Adaptive M
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3.2 Optimum MIMO Waveform Design fo
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3.4 Optimum MIMO Design for Target
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3.4 Optimum MIMO Design for Target
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3.5 Constrained Optimum MIMO Radar
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108 CHAPTER 3 Optimal and Adaptive
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3.6 Adaptive MIMO Radar 111SINR Los
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3.10 Problems 115[22] W. Y. Hsiang,
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3.10 Problems 1179. A constrained o
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120 CHAPTER 4 MIMO Radarperformance
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4.3 The MIMO Virtual Array 123separ
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4.4 MIMO Radar Signal Processing 12
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134 CHAPTER 4 MIMO RadarFIGURE 4-7T
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136 CHAPTER 4 MIMO Radar4.5.2 MIMO
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138 CHAPTER 4 MIMO Radarnamely, R
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140 CHAPTER 4 MIMO RadarTABLE 4-2 P
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142 CHAPTER 4 MIMO RadarSINR Loss (
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144 CHAPTER 4 MIMO Radar[10] H. V.
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Radar Applications of SparseReconst
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5.1 Introduction 149δ = M/N, the u
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152 CHAPTER 5 Radar Applications of
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5.2 CS Theory 163While the notion o
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5.2 CS Theory 165as , where is a b
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5.3 SR Algorithms 167than the norm
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5.3 SR Algorithms 169the value of o
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5.3 SR Algorithms 171takes special
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5.3 SR Algorithms 173The function f
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5.3 SR Algorithms 175of the TV norm
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5.3 SR Algorithms 177One can argue
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5.3 SR Algorithms 179probability ma
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5.3 SR Algorithms 181only a subset
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5.4 Sample Radar Applications 183pr
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5.4 Sample Radar Applications 185We
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5.4 Sample Radar Applications 187th
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5.4 Sample Radar Applications 189tu
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5.4 Sample Radar Applications 191Ra
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5.4 Sample Radar Applications 193Ta
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Spotlight SyntheticAperture RadarCH
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6.1 Introduction 213• Image quali
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6.2 Mathematical Background 215andf
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6.2 Mathematical Background 2172π
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220 CHAPTER 6 Spotlight Synthetic A
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6.4 Sampling Requirements and Resol
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6.4 Sampling Requirements and Resol
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6.5 Image Reconstruction 239FIGURE
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6.6 Image Metrics 241The contrast r
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6.6 Image Metrics 243Along-track am
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6.7 Phase Error Effects 245TABLE 6-
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260 CHAPTER 7 Stripmap SAR3 m SAR O
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262 CHAPTER 7 Stripmap SAR• RMA i
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264 CHAPTER 7 Stripmap SARH 0 (ω)
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268 CHAPTER 7 Stripmap SARTABLE 7-1
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274 CHAPTER 7 Stripmap SARFIGURE 7-
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276 CHAPTER 7 Stripmap SAR−500Sce
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7.3 Doppler Beam Sharpening Extensi
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288 CHAPTER 7 Stripmap SARand in th
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7.4 Range-Doppler Algorithms 291to
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7.4 Range-Doppler Algorithms 293For
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296 CHAPTER 7 Stripmap SAR−150Col
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300 CHAPTER 7 Stripmap SAR33 cycles
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302 CHAPTER 7 Stripmap SARCrossrang
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304 CHAPTER 7 Stripmap SARfrom freq
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7.5 Range Migration Algorithm 307r,
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310 CHAPTER 7 Stripmap SARkx (rad/m
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316 CHAPTER 7 Stripmap SARThe diffe
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320 CHAPTER 7 Stripmap SARLet us re
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322 CHAPTER 7 Stripmap SARThe resul
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324 CHAPTER 7 Stripmap SARscene. Th
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326 CHAPTER 7 Stripmap SARdriven by
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328 CHAPTER 7 Stripmap SARTABLE 7-3
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330 CHAPTER 7 Stripmap SARFIGURE 7-
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332 CHAPTER 7 Stripmap SAR7.10 REFE
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334 CHAPTER 7 Stripmap SAR6. [MATLA
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Interferometric SAR andCoherent Exp
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8.1 Introduction 3398.1.1 Organizat
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8.1 Introduction 341Rsabnv rw x ,w
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8.2 Digital Terrain Models 343FIGUR
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8.3 Estimating Elevation Profiles U
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352 CHAPTER 8 Interferometric SAR a
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8.5 InSAR Processing Steps 365ξ ta
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8.5 InSAR Processing Steps 3670.1 0
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8.5 InSAR Processing Steps 373While
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8.6 Error Sources 375The second met
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8.6 Error Sources 377If sufficient
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Adaptive Digital BeamformingCHAPTER
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9.1 Introduction 403c k = clutter s
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408 CHAPTER 9 Adaptive Digital Beam
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9.2 Digital Beamforming Fundamental
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9.3 Adaptive Jammer Cancellation 42
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9.5 Wideband Cancellation 447FIGURE
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454 CHAPTER 10 Clutter Suppression
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10.2 Space-Time Signal Representati
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10.5 STAP Fundamentals 4810−5−1
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10.6 STAP Processing Architectures
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10.7 Other Considerations 4911 CPIM
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10.9 Summary 49310.7.3 Computationa
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10.10 References 495[12] Fenner, D.
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10.11 Problems 497the PSD in Figure
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11.2 Colored Space-Time Exploration
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11.2 Colored Space-Time Exploration
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506 CHAPTER 11 Space-Time Coding fo
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11.8 References 525Cost reduction o
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11.9 Problems 52711.9.2 Equivalence
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530 CHAPTER 12 Electronic Protectio
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12.2 Electronic Attack 535TABLE 12-
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12.2 Electronic Attack 541variation
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12.5 Antenna-Based EP 557are adjust
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12.6 Transmitter-Based EP 561polari
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12.11 Summary 583TABLE 12-4Summary
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12.14 Problems 585[9] Stimson, G.W.
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Introduction to RadarPolarimetryCHA
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13.1 Introduction 591and precipitat
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13.1 Introduction 593S: target scat
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13.2 Polarization 597After substitu
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13.2 Polarization 599zFIGURE 13-4Po
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13.3 Scattering Matrix 601left-hand
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604 CHAPTER 13 Introduction to Rada
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13.4 Radar Applications of Polarime
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13.4 Radar Applications of Polarime
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13.5 Measurement of the Scattering
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13.5 Measurement of the Scattering
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13.8 References 623• Lee, Jong-Se
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13.8 References 625[32] Kraus, J.D.
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13.9 Problems 627with the result in
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Automatic Target RecognitionCHAPTER
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14.2 Unified Framework for ATR 633P
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14.3 Metrics and Performance Predic
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14.3 Metrics and Performance Predic
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14.4 Synthetic Aperture Radar 639TA
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14.4 Synthetic Aperture Radar 641Te
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14.4 Synthetic Aperture Radar 64314
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14.4 Synthetic Aperture Radar 645Un
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14.4 Synthetic Aperture Radar 64714
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14.4 Synthetic Aperture Radar 649se
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14.4 Synthetic Aperture Radar 651th
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754 CHAPTER 17 Advanced Processing
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17.3 Direct Signal and Multipath/Cl
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17.3 Direct Signal and Multipath/Cl
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17.4 Signal Processing Techniques f
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17.5 2D-CCF Sidelobe Control 787TAB
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17.6 Multichannel Processing for De
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17.10 References 815While far from
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17.10 References 817[20] Chetty, K.
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17.11 Problems 819[52] Gao, Z., Tao
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17.11 Problems 8215. Using the sign
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824 Appendix A: Answers to Selected
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ResolutionResolution withDimension
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830 IndexASAR. See Advanced SAR (AS
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832 IndexCross-polarization (XPOL),
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834 IndexExciter-based EP (cont.)fr
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836 IndexInterferencecolored noise,
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838 IndexMoving target indication (
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840 IndexPOI. See Probability of in
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842 IndexSaturated (constant amplit
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844 IndexStripmap SAR (cont.)remote
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846 IndexWWald sequential test, com
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