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Coherent DOA estimation via Toeplitz-based tensor decomposition
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AI
DOI：10.1016/j.aeue.2025.155984.png)
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Abstract
En 中文
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.
Keywords:
DOA estimation
tensor reconstruction
Toeplitz matrix
signal decorrelation
PARAFAC model
Journal
A
IF:
3.2
Papers:
5.6K
Citations:
8.3K
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