DOA and polarization estimation for compressed EMVS array with arbitrary sensor geometry

Zhu, Dongchen; Chen, Baixiao; Cheng, Xiancheng

doi:10.1016/j.dsp.2022.103778

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…In previous studies, the performance of the joint polarization DOA estimation algorithm mainly depends on the estimation accuracy and real-time. The subspace super-resolution estimation method represented by multiple signal classification (MUSIC) algorithm 13 , 14 and estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm 15 , 16 has become a noticeable research topic. MUSIC algorithm estimates the parameters of incident signals with high accuracy by searching the constructed spatial spectrum.…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional parameter estimation of uniform circular array electromagnetic vector sensor based on polarization-direction of arrival matrix

Wang,

He,

Han

et al. 2023

J. Appl. Rem. Sens.

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Uniform circular array electromagnetic vector sensor (UCA-EVS) can provide omnidirectional 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.

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