Emerging Trends in Radar: Robust Sparse Beamforming for Radar Array Processing

Abstract

In this article, the problem of robust sparse beamforming (RSB) design for a receive array is presented, based on minimax and maximin signal-to-interference-plus-noise ratio (SINR) optimization criteria. The framework hybridizes weighted l1-norm (or its squared version) regularization (to manage the beamvector's sparsity) with modern robust adaptive beamforming methods, when the uncertainty sets for the parameters in the SINR formula are convex and the feasible set of the beamvectors contains at least a cardinality constraint. In addition, three main challenges of the RSB designs are discussed, including the scenarios with nonconvex uncertainty sets for SINR parameters, general-rank covariance matrix of the signal of interest (SOI), and multiple statistically independent SOI sources. Finally, five future research directions for the RSB designs are pointed out.