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Coherent DOA estimation via Toeplitz-based tensor decomposition.

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Title: Coherent DOA estimation via Toeplitz-based tensor decomposition.
Authors: Ling, Yi1 (AUTHOR) y_l777@nudt.edu.cn, Shi, Junpeng1 (AUTHOR) shijunpeng20@nudt.edu.cn, Yi, Xianzhou1 (AUTHOR) xianzhouyi@whu.edu.cn, Shi, Shuyun1 (AUTHOR) shishuyun21@nudt.edu.cn
Source: AEU: International Journal of Electronics & Communications. Nov2025, Vol. 201, pN.PAG-N.PAG. 1p.
Subjects: Direction of arrival estimation, Decorrelation (Signal processing), Tensor algebra, Signal processing, Covariance matrices, Simulation methods & models
Abstract: Coherent signals encountered in practical electromagnetic environments present significant challenges for high-resolution direction-of-arrival (DOA) estimation. While conventional spatial smoothing-based tensor reconstruction methods neither fully exploit the structural properties of the covariance matrix nor exhibit satisfactory signal decorrelation performance. Therefore, we propose a Toeplitz-tensor-based decorrelation (TTD) algorithm. Specifically, we construct Toeplitz matrices using all the rows of the covariance matrix, which are then stacked as a tensor PARAFAC model. The backward tensor is then built to form an augmented one to improve the performance. Furthermore, we conduct a comprehensive performance analysis for the tensor rank and the computations. Simulation results verify that the TTD not only improves estimation accuracy but also demonstrates stronger resistance to signal correlation effects. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Coherent signals encountered in practical electromagnetic environments present significant challenges for high-resolution direction-of-arrival (DOA) estimation. While conventional spatial smoothing-based tensor reconstruction methods neither fully exploit the structural properties of the covariance matrix nor exhibit satisfactory signal decorrelation performance. Therefore, we propose a Toeplitz-tensor-based decorrelation (TTD) algorithm. Specifically, we construct Toeplitz matrices using all the rows of the covariance matrix, which are then stacked as a tensor PARAFAC model. The backward tensor is then built to form an augmented one to improve the performance. Furthermore, we conduct a comprehensive performance analysis for the tensor rank and the computations. Simulation results verify that the TTD not only improves estimation accuracy but also demonstrates stronger resistance to signal correlation effects. [ABSTRACT FROM AUTHOR]
ISSN:14348411
DOI:10.1016/j.aeue.2025.155984