Principles of Modern Radar - Volume 2 1891121537

710 CHAPTER 16 Human Detection With Radar: Dismount Detection16.1.3 AcronymsATR appendage-to-toe ratioCCD coherent change detectionCPI coherent processing intervalFFT fast Fourier transformGMTI ground moving target indicationHS heel strikeIED improvised explosive devicesMDV minimum detectable velocityPDF probability density functionPRI pulse repetition intervalRCS radar cross sectionSAR synthetic aperture radarSIR signal-to-interference ratioSNR signal-to-noise ratioSTAP space-time adaptive processingTL toe liftUAS unmanned aerial system16.2 CHARACTERIZING THE HUMANRADAR RETURNHumans are complicated targets due to the intricate motion of many parts, all moving atdifferent speeds and along different trajectories, depending on the type of activity engagedin, such as walking, running, jumping or playing sports [35–38]. Research has shown thathuman kinematics even change depending on whether or not a person is carrying a load,and how this load is being carried [39–41]. Indeed, some optimistic researchers haveeven stated that the periodic, bipedal nature of human walking is so unique that in thefuture human gait may be used as a biometric parameter for identification, similar to theway fingerprints and hand geometry are used today [42–44]. While the wide variation inhuman activity makes the development of a universal model characterizing human motiondifficult, it also provides a basis for the development of radar signal processing algorithmscapable of discriminating a variety of human activities.When a radar signal interacts with a target, the reflected signal depends on a number offactors, including the target’s RCS and time-varying range, among others. For rigid-bodytargets such as the chassis of a vehicle, virtually all points comprising the target move atthe same speed relative to the radar, leading to a constant Doppler shift in the frequencyof the reflected signal received by the radar. If, however, there is motion, vibration, orrotation, in addition to bulk translation, then sidebands about the target’s bulk Doppler frequencyare generated [45–47]. Such micro-Doppler signals may be the result of reflectionsfrom the wheels of vehicles, aircraft propellers, and helicopter blades, as well as humanmotion.Early efforts to characterize the human micro-Doppler signature began in 1997 withwork by Weir [48,49], in which the velocity profile of a human target (what Weir termeda gait velocitygram) was observed to contain distinct features that could be used to garnerinformation about the gait. Later work by Geisheimer [50] involved the use of chirplet