Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

Huaxin Yu,Gang Yang,Chenghua Wang,Xiaofeng Qiu,Xiaofei Zhang

doi:10.1155/2015/297572

Abstract

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

Highlights

Array signal processing has received a significant amount of attention during the last decades due to its wide application in radar, sonar, radio astronomy, and satellite communication [1]
The direction of arrival (DOA) estimation of signals impinging on an antenna array is a fundamental problem in array signal processing, and many DOA estimation methods [2,3,4,5,6,7] have been proposed for its solution
It has been proved that twodimensional multiple signal classification (MUSIC) (2D-MUSIC) algorithm [13] can be used for 2D-DOA estimation

Summary

Introduction

Array signal processing has received a significant amount of attention during the last decades due to its wide application in radar, sonar, radio astronomy, and satellite communication [1]. Parallel factor analysis (PARAFAC) in [12], which is called trilinear decomposition method, was proposed for 2D-DOA estimation for uniform rectangular array, and it has better angle estimation performance than ESPRIT. The problem of 2D-DOA estimation for rectangular array is linked to compressive sensing trilinear model. We formulate a sparse recovery problem through the estimated compressed direction matrices and apply the orthogonal matching pursuit (OMP) [26] to resolve it for 2D-DOA estimation. The proposed method has much lower computational complexity than conventional trilinear decomposition method [12] and 2D-MUSIC algorithm and requires much smaller storage capacity. Where aij and bij are the (i, j) element of the matrices A and B, respectively

Data Model

Numerical Simulations

Conflict of Interests