Crosscorrelation and DOA Estimation for L-Shaped Array via Decoupled Atomic Norm Minimization

Yu Zhang,Yinan Sun,Yu Tao,Gong Zhang,Xinhai Wang,Jun Cai

doi:10.1155/2021/6659679

Abstract

A novel two-phase method for two-dimensional (2D) direction-of-arrival (DOA) estimation with L-shaped array based on decoupled atomic norm minimization (DANM) is proposed in this paper. In the first phase, given the sample crosscorrelation matrix, the gridless DANM technique considering the noise and finite snapshots effects is employed to exploit the structure and sparse properties of the crosscorrelation matrix. The resulting DANM-based algorithm not only enables the crosscorrelation matrix reconstruction (CCMR) but also reconstructs the covariance matrix of the L-shaped array. Hence, sequentially, in the second phase, the conventional 2D DOA estimators for the L-shaped array can be adopted for the angle estimation. With appropriate 2D DOA estimators, the resulting proposed algorithms can not only achieve better performance but also detect more source number, compared with conventional crosscorrelation-based DOA estimators. Moreover, the proposed method, termed CCMR-DANM, not only has blind characteristic that it does not require the prior information of source numbers but also is more efficient than the existing CCMR-based counterparts. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method.

Highlights

The problem of two-dimensional (2D) direction-of-arrival (DOA) estimation plays an important role in array signal processing and has attracted much interest in the area of wireless communications, radar and sonar [1,2,3,4,5,6,7]
Since the L-shaped array can achieve better estimation performance than others, it has attracted a lot of attentions and many corresponding algorithms for 2D DOA estimation have been proposed in last several decades [8,9,10,11,12,13,14,15]
The last is based on the crosscorrelation of the L-shaped array, which is naturally contaminated by less noise, compared with the covariance matrix

Summary

Introduction

The problem of two-dimensional (2D) direction-of-arrival (DOA) estimation plays an important role in array signal processing and has attracted much interest in the area of wireless communications, radar and sonar [1,2,3,4,5,6,7]. For 2D DOA estimation, many array structures, such as rectangular arrays, circular arrays, and L-shaped arrays, have been developed Among these arrays, since the L-shaped array can achieve better estimation performance than others, it has attracted a lot of attentions and many corresponding algorithms for 2D DOA estimation have been proposed in last several decades [8,9,10,11,12,13,14,15]. The second is to jointly estimate the two angles based on the covariance matrix of the L-shaped array They can detect more source numbers than the first ones and do not need an extra pairing [8, 9]. The sample matrix cannot equip the ideal structure of the ideal matrix, which leads to performance degradation especially in low SNR and small number of snapshots [19]

Objectives

Methods

Results

Conclusion