Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

Peng Chen,Zhenxin Cao,Linxi Liu,Man Feng,Zhimin Chen

doi:10.3390/electronics7120424

Peng Chen, Zhenxin Cao + Show 3 more

Open Access

https://doi.org/10.3390/electronics7120424

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Journal: Electronics	Publication Date: Dec 11, 2018
Citations: 15	License type: CC BY 4.0

Affiliation: Southeast University, Shanghai Dianji University

Abstract

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

Highlights

IntroductionThe direction-of-arrival (DOA) estimation problems have been widely investigated
In array systems, the direction-of-arrival (DOA) estimation problems have been widely investigated
The simulation results about the DOA estimation in the uniform linear array (ULA)

Summary

Introduction

The direction-of-arrival (DOA) estimation problems have been widely investigated. To estimate the DOA, the beamspace-based methods have been proposed [9]. The subspace-based methods for DOA estimation only consider the subspaces of signals and noise, and the signal sparsity has not been considered. The compressed-sensing (CS)-based methods have been proposed to exploit the sparsity of signals in the spatial domain [19,20,21,22,23,24,25,26,27]. A novel reconstruction method named unknown MC effect (SDMC) is proposed and estimating the MC coefficients and the sparse signals iteratively. The CS-based system model with unknown MC effect: A system model considering both the MC effect and the signal sparsity is proposed and converts the DOA estimation problem into a sparse reconstruction problem.

System Model without MC Effect

System Model with MC Effect

Sparse System Model

DOA Estimation with Unknown MC Effect

Simulation Results

Proposed Method

Conclusions