Abstract
In this paper, we address the joint estimation problem of elevation, azimuth, and polarization with nested array consists of complete six-component electromagnetic vector-sensors (EMVS). Taking advantage of the tensor permutation, we convert the sample covariance matrix of the receive data into a tensorial form which provides enhanced degree-of-freedom. Moreover, the parameter estimation issue with the proposed model boils down to a Vandermonde constraint Canonical Polyadic Decomposition problem. The structured least squares estimation of signal parameters via rotational invariance techniques is tailored for joint auto-pairing elevation, azimuth, and polarization estimation, ending up with a computational efficient method that avoids exhaustive searching over spatial and polarization region. Furthermore, the sufficient uniqueness analysis of our proposed approach is addressed, and the stochastic Cramér-Rao bound for underdetermined parameter estimation is derived. Simulation results are given to verify the effectiveness of the proposed method.
Highlights
Electromagnetic vector-sensor (EMVS) has been widely used in a variety of applications such as localization, tracking, and beamforming [1,2,3]
3.2 Proposed tensor modeling method Different from the existing tensor modeling methods in [30] and [31], we introduce the property of tensor permutation to establish a tensor model of the nested EMVS array
We examine the performance of the Alternating Least Squares (ALS)-Canonical Polyadic Decomposition (CPD) with our proposed model (38), SS-CPD and structured least squares (SLS)-Vandermonde constrained CPD (VCPD) methods based on the nested EMVS array
Summary
Electromagnetic vector-sensor (EMVS) has been widely used in a variety of applications such as localization, tracking, and beamforming [1,2,3]. In order to utilize the aforementioned benefits, the DOA and polarization estimation with EMVS arrays could be cast as a multiple-parameter estimation problem which, turns out to be much more complicated than the scalar-sensor array case. They demand a time-consuming multi-dimensional searching procedure [11]. This method divides the nested EMVS array into several subarrays with respect to different polarization states It builds a tensor composed of multiple local covariances, followed by the CPD to obtain DOA and polarization estimations.
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