Joint DOA, range and polarization estimation based on geometric algebra for near-field non-circular source with a symmetric MIMO array

Wang, Xiangyang; Lv, Xiaolu; Wang, Rui

doi:10.1016/j.dsp.2023.103963

Cited by 4 publications

(1 citation statement)

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“…In the past few decades, direction of arrival (DOA) estimation has been demonstrated to play a significant role in radar, 1 – 3 communication, 4 , 5 seismic survey, 6 , 7 and other fields as an important branch of signal processing. Electromagnetic vector sensor (EVS), 8 which can not only provide the spatial orientation information of electromagnetic waves but also receive the polarization information, has gained huge success in the field of radar array signal processing 9 …”

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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