ABSTRACT
A large number of compressed sensing–based algorithms have been applied to radar systems [1–9]. Such algorithms reconstruct the target scene from fewer measurements than traditional algorithms. Baraniuk and Steeghs [1] demonstrated that compressed sensing can eliminate the need for a matched filter at the receiver and that it has the potential to reduce the required sampling rate. Liu, Wei, and Li [2] presented an adaptive clutter suppression algorithm for airborne random pulse repetition interval radar by using prior knowledge of the clutter boundary in the Doppler spectrum. Yang and Zhang [3] focused on monostatic chaotic multiple-input multiple-output radar systems and analyzed, theoretically and numerically, the performance of sparsity-exploiting algorithms for the parameter estimation of targets at low signal-to-noise ratios (SNRs). In the context of synthetic aperture radar, compressed sensing–based data acquisition and imaging algorithms are given in [4–9].
