DOA and Polarization Parameters Estimation by Exploiting Canonical Polyadic Decomposition of Tensors

Long Liu,Ling Wang,Zhaolin Zhang,Jian Xie

doi:10.1155/2019/7389306

Abstract

A new algorithm to estimate the direction of arrival (DOA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors is presented by exploiting the canonical polyadic decomposition (CPD) of tensors. In addition to spatial and temporal diversities, further information from the polarization domain is considered and used in this paper. Estimation errors of these parameters are evaluated by the Cramér-Rao lower bound (CRB) benchmark, in the presence of additive white Gaussian noise (AWGN). The superiority of the proposed algorithm is shown by comparing with the derivative algorithms of MUSIC and ESPRIT. In the proposed algorithm, the parameters can be estimated by virtue of the diversities of the spatial and polarization belonging to the factor matrices, rather than the conventional subspace which is the foundation of MUSIC and ESPRIT. Additionally, the classical CPD algorithm based on Alternating Least Squares (ALS) is introduced to verify the efficacy of the proposed CPD algorithm. Results demonstrate that when the number of snapshots is greater than 50, the proposed algorithm requires a smaller number of snapshots to achieve a high level of performance, compared against the subspace-based algorithms and the ALS-based algorithm. Furthermore, in the matter of the array with a small number of sensors, the discovered advantage concerning the Root Mean Square Error (RMSE) in estimating the DOA and the polarization state of the signal is noteworthy.

Highlights

Signal parameters estimation is of great significance in many applications such as satellite navigation, wireless communication, radar, and sonar
The spatial parameter refers to the direction of arrival (DOA) of the signal, while the polarization auxiliary angle and the polarization phase difference of the signal can be regarded as the polarization parameters
Among signal parameters estimation algorithms based on matrix operations, some depend on exhaustive search, e.g., multiple signal classification (MUSIC) [1], whereas others do not, e.g., estimation of the signal parameters via rotational invariance techniques (ESPRIT) [2] and root-MUSIC [3]

Summary

Introduction

Signal parameters estimation is of great significance in many applications such as satellite navigation, wireless communication, radar, and sonar. An array of multiple sensors placed in different spatial locations is used to estimate the signal parameters, which mainly include spatial and polarization parameters of the signal. Some methods exploiting the signalsubspace embodied by MUSIC, ESPRIT and their derivative algorithms have been introduced in [4,5,6,7,8,9,10,11,12], in which the tensor-based data model has been combined in [10,11,12]. Based on the Higher-Order Singular Value Decomposition (HOSVD) of the measurement tensor, two algorithms of parameters estimation combined with ESPRIT have been developed in [10]. In [11, 12], several tensor MUSIC methods based on HOSVD have been derived.

Objectives

Results

Conclusion