Signal parameter estimation through hierarchical conjugate gradient least squares applied to tensor decomposition

Long Liu; Ling Wang; Jian Xie; Yuexian Wang; Zhaolin Zhang

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자료유형: 학술저널

저자정보: Long Liu Ling Wang Jian Xie Yuexian Wang Zhaolin Zhang

저널정보: 한국전자통신연구원 [ETRI] ETRI Journal ETRI Journal 제42권 제6호

발행연도: 2020.12

수록면: 922 - 931 (0page)

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A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector‐sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS‐based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable.

참고문헌 (33)

참고문헌 신청

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W. Suleiman / 2016 / Performance analysis of the decentralized eigendecomposition and ESPRIT algorithm / IEEE Trans. Signal Process 64(9):2375~2386

S. Y. Liu / 2019 / Hierarchical principle-based iterative parameter estimation algorithm for dual-frequency signals / Circuits Syst. Signal Process 18(7):3251~3268

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