Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrays

TL;DR

This paper introduces a Siamese neural network with a sparse augmentation layer to improve single-snapshot DOA estimation accuracy in sparse linear arrays, addressing challenges like high sidelobe levels and limited training data.

Contribution

The paper proposes a novel SNN architecture with a sparse augmentation layer specifically designed for sparse array DOA estimation, enhancing feature embedding and accuracy.

Findings

Improved DOA estimation accuracy demonstrated through experiments.

Enhanced feature embedding via the sparse augmentation layer.

Effective performance in dynamic environments with limited snapshots.

Abstract

Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna failures, leading to challenges such as high sidelobe levels and compromised accuracy in direction-of-arrival (DOA) estimation. Despite deep learning's success in tasks such as DOA estimation, the need for extensive training data to increase target numbers or improve angular resolution poses significant challenges. In response, this paper presents a novel Siamese neural network (SNN) featuring a sparse augmentation layer, which enhances signal feature embedding and DOA estimation accuracy in sparse arrays. We demonstrate the enhanced DOA estimation performance of our approach through detailed feature analysis and performance evaluation. The code for this study is available at https://github.com/ruxinzh/SNNS_SLA.