Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching

In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), swiftly adapt both configuration and excitation weights in accordance the dynamic environment for enhancing interference suppression. order achieve an implementable design reconfiguration, RCAS is conducted framework switching, whereby full aperture collectively divided into separate groups only one each group switched on connect with processing channel. A set deterministic arrays good quiescent beampatterns first designed by data collected among them while maintaining resilient Subsequently, tailored specific calculated reconfigured based information extracted from data. The devised exclusive cardinality-constrained optimization, which reformulated introducing auxiliary variable combined piece-wise linear function approximate $l_0$-norm function. regularization formulation proposed solve problem iteratively eliminate requirement feasible initial search point. rigorous theoretical analysis conducted, proves that algorithm essentially equivalent transformation original optimization. Simulation results validate effectiveness strategy.