|M.Sc Student||Omer Bar-Ilan|
|Subject||Sub-Nyquist Radar via Doppler Focusing|
|Department||Department of Electrical Engineering||Supervisor||Full Professor Eldar Yonina|
|Full Thesis text|
We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets and estimate their locations, where the targets are sparsely populated in the radar's unambiguous time-frequency region. Classic radar signal processing methods treat this problem, but require sampling and processing at the signal's Nyquist rate. In this work we seek a solution which is independent of the Nyquist rate. Several past works employ Compressed Sensing (CS) algorithms for 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 a signal-to-noise ratio (SNR) improvement which scales linearly with P, obtaining good detection performance even at SNR 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. We use the Finite Rate of Innovation as an underlying framework in which to model our signals, in a way which facilitates recovery with a limited number of samples. 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 radar prototype.