IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays

TL;DR

This paper introduces IHT-Net, a neural network inspired by iterative hard thresholding, designed for single-snapshot DOA estimation with sparse linear arrays, improving accuracy and efficiency over traditional methods.

Contribution

The paper proposes a novel IHT-inspired neural network that replaces high-cost operations with autoencoders, enhancing single-snapshot DOA estimation in automotive radar applications.

Findings

IHT-Net achieves faster convergence than traditional IHT algorithms.

The method improves DOA estimation accuracy in sparse array scenarios.

Numerical results demonstrate superior performance of IHT-Net over existing techniques.

Abstract

Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, where multiple snapshots aren't accessible, and the importance of minimizing hardware costs. Low-rank Hankel matrix completion has been proposed to interpolate the missing elements in SLAs. However, the solvers of matrix completion, such as iterative hard thresholding (IHT), heavily rely on expert knowledge of hyperparameter tuning and lack task-specificity. Besides, IHT involves truncated-singular value decomposition (t-SVD), which has high computational cost in each iteration. In this paper, we propose an IHT-inspired neural network for single-snapshot DOA estimation with SLAs, termed IHT-Net. We utilize a recurrent neural network structure to parameterize the IHT algorithm. Additionally, we integrate shallow-layer autoencoders to replace t-SVD, reducing computational overhead while generating a novel optimizer through supervised learning. IHT-Net maintains strong interpretability as its network layer operations align with the iterations of the IHT algorithm. The learned optimizer exhibits fast convergence and higher accuracy in the full array signal reconstruction followed by single-snapshot DOA estimation. Numerical results validate the effectiveness of the proposed method.