Compressed Radar via Doppler Focusing

We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets, sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem, but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here we describe a sub-Nyquist sampling and recovery approach called Doppler focusing which performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size which does not increase with number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement which scales linearly with P, obtaining good detection performance even at SNR as low as 25dB. It can easily incorporate clutter rejection capabilities, and handle targets with large dynamic range. The recovery is based on the Xampling framework, which allows sub-Nyquist analog-to-digital conversion. The entire digital recovery process is also performed at the low rate. Finally, our approach is implemented in hardware using a Xampling radar prototype.