Adaptive Clutter Suppression for Airborne Random Pulse Repetition Interval Radar Based on Compressed Sensing

We present an adaptive clutter suppression method for airborne random pulse repetition interval radar by using prior knowledge of clutter boundary in Doppler spectrum. In this method, by exploiting the intrinsic sparsity, compressed sensing based on iterative grid optimization (CS-IGO) is applied to directly recover the clutter spectrum with only the test range cell instead of nonhomogeneous training data from adjacent range cells. Since the sensing matrix and clutter spectrum obtained by CS-IGO are well adapted to the data, the prewhitening filter can be effectively obtained to cancel the mainlobe clutter. Further, the clutter residue can be suppressed by the iterative reweighted l1 minimization to enhance the target response. Simulation results show that the approach is capable of effective suppression of clutter and precise recovery of targets’ unambiguous spectrum, offering a high performance of output signal to clutter and noise ratio.