Nonlinear-Phase, Model-Based Human Detector for Radar (Preprint)

摘要

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.