7 August 2023 Multi-dimensional parameter estimation of uniform circular array electromagnetic vector sensor based on polarization-direction of arrival matrix
Liyuan Wang, Huafeng He, Xiaofei Han, Yaomin He, Zheng Li
Author Affiliations +
Abstract

Uniform circular array electromagnetic vector sensor (UCA-EVS) can provide omni-directional and high-resolution azimuth information and polarization information, which has been widely concerned in the field of radar signal processing. Aiming at the joint estimation of direction of arrival (DOA) and polarization parameters of coherent sources via UCA-EVS, a multi-dimensional parameter estimation method with low complexity—polarization-DOA matrix method—is proposed. First, the rank of array covariance matrix is recovered by axial virtual translation, and then the direction and polarization parameters of the signal are estimated by using the eigenvalues and eigenvectors of the matrix based on constructing polarization-DOA matrix. Different from the traditional DOA matrix method, the proposed algorithm can not only estimate the azimuth information of the signals but also provide the polarization information of the targets, and the estimated parameters can be matched automatically. At the same time, it can estimate the parameters only by using the information of three elements, which can save hardware resources. In addition, the proposed method does not need to search for spectral peaks, which not only greatly reduces the computational complexity but also loses the estimation accuracy. Simulation results verify the feasibility of the proposed algorithm.

© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)
Liyuan Wang, Huafeng He, Xiaofei Han, Yaomin He, and Zheng Li "Multi-dimensional parameter estimation of uniform circular array electromagnetic vector sensor based on polarization-direction of arrival matrix," Journal of Applied Remote Sensing 17(3), 036503 (7 August 2023). https://doi.org/10.1117/1.JRS.17.036503
Received: 13 March 2023; Accepted: 24 July 2023; Published: 7 August 2023
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KEYWORDS
Polarization

Matrices

Covariance matrices

Monte Carlo methods

Signal to noise ratio

Error analysis

Computer simulations

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