A Nonlinear-Phase, Model-Based Human Detector for Radar (Preprint)
Techincal paper 8 Sep 2006-31 Aug 2009
GEORGIA INST OF TECH ATLANTA SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING
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Radar offers unique advantages over other sensors for the detection of humans, such as remote operation during virtually all weather and lighting conditions. Many current radar-based human detection systems employ some type of Fourier analysis, such as spectrograms. However, in many environments, the signal-to-noise ratio SNR for human targets is quite low and the spectrogram is almost completely masked by clutter. Furthermore, Fourier-based techniques assume a linear target phase, whereas human targets have a highly nonlinear phase history. The resulting phase mismatch causes significant SNR loss in the detector itself. In this paper, human modeling is used to derive a more accurate non-linear approximation to the true target phase history. The likelihood ratio is optimized over unknown model parameters to enhance detection performance. Cramer-Rao bounds CRB on parameter estimates and receiver operating characteristic ROC curves are used to validate analytically the performance of the proposed method and to evaluate simulation results.
- Optical Detection and Detectors