Sub-Nyquist Radar via Doppler Focusing

TL;DR

This paper introduces Doppler focusing, a sub-Nyquist radar technique that reduces sampling rate, improves noise robustness, and simplifies digital processing for target detection in sparse environments.

Contribution

It presents a novel Doppler focusing method that enables low-rate sampling, no restrictions on the transmitter, and efficient CS recovery with a fixed dictionary size, outperforming prior approaches.

Findings

Achieves accurate target detection at SNR as low as -25dB.

Reduces sampling rate through Xampling framework.

Maintains fixed dictionary size regardless of pulse number.

Abstract

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 addresses all of these problems: it 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 increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys an SNR increase which scales linearly with P, obtaining good detection performance even at SNRs as low as -25dB. The recovery is based on the Xampling framework, which allows reducing the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling prototype.