Sparse Array Beamformer Design for Active and Passive Sensing

TL;DR

This paper proposes a hybrid sparse array beamformer design that adapts to dynamic environments to enhance signal-to-interference and noise ratio using convex optimization, machine learning, and structured sparsity.

Contribution

It introduces a novel hybrid approach combining environment-dependent and independent designs with convex optimization and machine learning techniques.

Findings

Improved SNR in dynamic environments

Effective sparse array configurations for wideband signals

Integration of data-driven and environment-aware methods

Abstract

Sparse sensor placement, with various design objectives, has successfully been employed in diverse application areas, particularly for enhanced parameter estimation and receiver performance. The sparse array design criteria are generally categorized into environment-independent and environment-dependent performance metrics. The former are largely benign to the underlying environment and, in principle, seek to maximize the spatial degrees of freedom by extending the coarray aperture. Environment-dependent objectives, on the other hand, consider the operating conditions characterized by emitters and targets in the array field of view, in addition to receiver noise. In this regard, applying such objectives renders the array configuration as well as the array weights time-varying in response to dynamic and changing environment. This work is geared towards designing environment-dependent sparse array beamformer to improve the output signal-to-interference and noise ratio using both narrowband and wideband signal platforms. One key challenge in implementing the data-dependent approaches is the lack of knowledge of exact or estimated values of the data autocorrelation function across the full sparse array aperture. At the core of this work is to address the aforementioned issues by devising innovative solutions using convex optimization and machine learning tools, structured sparsity concepts, low rank matrix completion schemes and fusing the environment-dependent and environment-independent deigns by developing a hybrid approach.