2024/05/15 by Farhang Yeganegi, Arian Eamaz, Yeganegi, Farhang +5
Computer Science · Engineering · #CCD and CMOS Imaging Sensors #FOS: Electrical engineering #Integrated Circuits and Semiconductor Failure Analysis #Signal Processing (eess.SP) #VLSI and Analog Circuit Testing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.09712
openalex publication_date 2024/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.