Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching

TL;DR

This paper introduces a novel adaptive sparse array beamformer design called RCAS that dynamically reconfigures antenna arrays for improved interference suppression using a regularized switching framework and cardinality-constrained optimization.

Contribution

The paper presents a new RCAS method for adaptive sparse array design that efficiently reconfigures antenna arrays in dynamic environments with a novel optimization approach.

Findings

RCAS effectively enhances interference suppression in simulations.

The optimization approach converges reliably without initial feasible points.

Theoretical analysis confirms the equivalence to the original problem.

Abstract

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