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Proximal constrained waveform design algorithms for cognitive radar STAP

Waveform design is an important component of the fully adaptive radar construct. In this paper we consider waveform design for radar space time adaptive processing (STAP), accounting for the waveform dependence of the clutter correlation matrix. Due to this dependence, in general, the joint problem of receiver weight vector optimization and radar waveform design is intractable. To addresses scarcity of representative data in range cells for estimating the STAP correlation matrices, we consider proximal constrained alternating minimization. These algorithms, at each step, regularize and optimize the STAP weight vector and the waveform iteratively. Unlike traditional STAP techniques, these algorithms are numerically stable, and can be used in the practical training data starved STAP scenarios. Our simulations reveal a non-increasing error variance at the output of the filter.