Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays

TL;DR

This paper introduces Sparse-SubspaceNet, a deep learning approach that improves direction-of-arrival estimation for sparse, miscalibrated arrays with coherent sources by learning to reconstruct virtual array covariances.

Contribution

It presents a novel deep learning framework that enables subspace DoA recovery from miscalibrated sparse arrays with coherent sources, overcoming limitations of traditional methods.

Findings

Effective DoA estimation with coherent sources

Robustness to array miscalibration

Preserves interpretability of subspace methods

Abstract

Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a virtual large uniform linear array (ULA), which is then processed by subspace DoA estimators. However, these method assume that the signals are non-coherent and the array is calibrated; the latter often challenging to achieve in sparse arrays, where one cannot access the virtual array elements. In this work, we propose Sparse-SubspaceNet, which leverages deep learning to enable subspace-based DoA recovery from sparse miscallibrated arrays with coherent sources. Sparse- SubspaceNet utilizes a dedicated deep network to learn from data how to compute a surrogate virtual array covariance that is divisible into distinguishable subspaces. By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.