Sparse Array Design for Direction Finding using Deep Learning

TL;DR

This paper reviews deep learning approaches for designing sparse arrays in direction finding, highlighting applications in radar, wireless communications, and integrated sensing, with a focus on performance and robustness.

Contribution

It provides a comprehensive overview of DL-based sparse array design methods across multiple applications, emphasizing new techniques and their comparative performance.

Findings

DL methods improve sparse array design efficiency

Deep learning enhances robustness against data imperfections

Numerical experiments demonstrate superior performance of DL approaches

Abstract

In the past few years, deep learning (DL) techniques have been introduced for designing sparse arrays. These methods offer the advantages of feature engineering and low prediction-stage complexity, which is helpful in tackling the combinatorial search inherent to finding a sparse array. In this chapter, we provide a synopsis of several direction finding applications of DL-based sparse arrays. We begin by examining supervised and transfer learning techniques that have applications in selecting sparse arrays for a cognitive radar application. Here, we also discuss the use of meta-heuristic learning algorithms such as simulated annealing for the case of designing two-dimensional sparse arrays. Next, we consider DL-based antenna selection for wireless communications, wherein sparse array problem may also be combined with channel estimation, beamforming, or localization. Finally, we provide an example of deep sparse array technique for integrated sensing and communications (ISAC) application, wherein a trade-off of radar and communications performance makes ISAC sparse array problem very challenging. For each setting, we illustrate the performance of model-based optimization and DL techniques through several numerical experiments. We discuss additional considerations required to ensure robustness of DL-based algorithms against various imperfections in array data.