Principles of Modern Radar - Volume 2 1891121537

708 CHAPTER 16 Human Detection With Radar: Dismount Detectionhuman identification techniques will be presented in Section 16.3. The technical challengesinvolved with human detection will be quantitatively examined in Section 16.4. Section16.5 outlines some of the recent novel ideas put forward to advance dismount detectiontechnologies, while Section 16.6 provides a brief summary of the chapter, Section 16.7recommends sources for additional reading, and Section 16.8 provides a detailed list ofrelevant references. A number of exercises designed to test the reader’s comprehensionare included in Section 16.9.16.1.1 Key Points• Human targets are difficult to detect due to their low radar cross section and low velocity.• Humans often fall below the MDV of ground moving target indicators and are easilymasked by ground clutter.• Models of human kinematics, such as the Boulic-Thalmann walking model, are beingused to analytically compute the expected human radar return.• The development of human models including a variety of human motion, such asrunning, jumping, or crawling, as well as accurate human cross section models are stilla subject of significant research.• The unique nature of human bipedal motion is the cause of micro-Doppler features inthe human spectrogram, which may be used to identify and classify detected targets.• STAP is a key technique for mitigating the effects of clutter but by itself is not sufficientto ensure the detection of dismounts in adverse clutter environments.• The nonlinear phase history of human targets, caused by micro-Doppler effects, resultsin an inherent signal-to-noise ratio (SNR) loss in the radar detector. The consequenceis a loss in dismount detection performance.• Novel techniques recently proposed include exploiting results from gait analysis asa priori knowledge that can be incorporated in human detection and classificationalgorithms.16.1.2 NotationA = sinusoidal approximation to human model parameter: factor in amplitude termA L = Boulic-Thalmann model parameter: amplitude of lateral translation of torsoA FB = Boulic-Thalmann model parameter: amplitude of forward/backwardtranslation of torsoATR = appendage to torso ratioa t = received signal amplitudeC 1 = sinusoidal approximation to human model parameter: factor relating to target rangeC 2 = sinusoidal approximation to human model parameter: amplitude of torso motionC 3 = sinusoidal approximation to human model parameter: torso frequencyC 4 = sinusoidal approximation to human model parameter: torso phasec = speed of lightd bp = dimension of a human body partD b = Boulic-Thalmann model parameter: duration of balanceD c = Boulic-Thalmann model parameter: duration of cycleD ds = Boulic-Thalmann model parameter: duration of double supportD s = Boulic-Thalmann model parameter: duration of support