Deep Learning-Enabled One-Bit DoA Estimation

TL;DR

This paper introduces a deep learning-based method for direction of arrival estimation using one-bit quantized measurements, leveraging covariance recovery and LISTA to improve accuracy in severely quantized scenarios.

Contribution

It presents a novel deep unrolling approach for DoA estimation from one-bit data, combining covariance recovery with LISTA for enhanced performance.

Findings

Effective covariance recovery from one-bit data demonstrated.

LISTA-based algorithm accurately estimates target locations.

Performance bound linked to covariance estimation error.

Abstract

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We present a compressed sensing formulation for DoA estimation from one-bit data in which estimating target DoAs requires recovering a sparse signal from a limited number of severely quantized linear measurements. In particular, we exploit covariance recovery from one-bit dither samples. To recover the covariance of transmitted signal, the learned iterative shrinkage and thresholding algorithm (LISTA) is employed fed by one-bit data. We demonstrate that the upper bound of estimation performance is governed by the recovery error of the transmitted signal covariance matrix. Through numerical experiments, we demonstrate the proposed LISTA-based algorithm's capability in estimating target locations. The code employed in this study is available online.