Adaptive Compressive Sensing and Processing of Delay-Doppler Radar Waveforms

Abstract

Compressive sensing and processing handles high-resolution delay-Doppler radar measurements using a low sampling rate, simple receiver design, and inexpensive processing when Nyquist rate sensing and processing becomes impractical. The benefits of compressive sensing and processing are, however, offset by an increase in estimation error that is introduced when processing compressed measurements versus measurements sampled at the Nyquist rate. In this work, an adaptive compressive sensing and processing method is proposed for the radar tracking problem. The adaptive scheme naturally incorporates information on target state that is readily available from a particle filter based tracker. The proposed method is shown to improve tracking performance over nonadaptive compressive sensing and processing, while maintaining low-sampling rates, a computationally inexpensive operation, and a simple receiver design.