Hybrid Sparse Array Beamforming Design for General Rank Signal Models

TL;DR

This paper introduces a hybrid sparse array design method for receive beamforming that optimizes MaxSINR performance in interference-rich environments, especially for limited aperture scenarios with multiple sources.

Contribution

It proposes a novel hybrid approach formulating array design as a QCQP with l_1-norm penalization, enhancing array configurability and performance over existing methods.

Findings

Effective array configurability demonstrated in simulations

Outperforms uniform and other sparse array designs

Improves MaxSINR in interference environments

Abstract

The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR) for both single point source and multiple point sources, operating in an interference active environment. Unlike existing sparse design methods which either deal with structured environment-independent or non-structured environment-dependent arrays, our method is a hybrid approach and seeks a full augumentable array that optimizes beamformer performance. This approach proves important for limited aperture that constrains the number of possible uniform grid points for sensor placements. The problem is formulated as quadratically constraint quadratic program (QCQP), with the cost function penalized with weighted l_1-norm squared of the beamformer weight vector. Simulation results are presented to show the effectiveness of the proposed algorithms for array configurability in the case of both single and general rank signal correlation matrices. Performance comparisons among the proposed sparse array, the commonly used uniform arrays, arrays obtained by other design methods, and arrays designed without the augmentability constraint are provided.