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Structured Tensor Reconstruction for Coherent DOA Estimation
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DOI：10.1109/LSP.2022.3190768.png)
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摘要
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Existing tensor-based coherent direction-of-arrival (DOA) estimation methods adopting spatial smoothing to decorrelate the coherent tensor statistics usually lead to a poor decorrelation performance. In this letter, we propose a structured tensor reconstruction method for two-dimensional coherent DOA estimation, which then avoids the inefficient spatial smoothing. In particular, after investigating the structural property of the four-dimensional incoherent covariance tensor, we propose a tensorial Hermitian Toeplitz mapping rule to reconstruct a structured covariance tensor from the rank-deficient coherent covariance tensor statistics. It is theoretically proved that, the reconstructed covariance tensor admits a decorrelated canonical polyadic model with a tensorial Hermitian Toeplitz structure, whose decomposition ensures a closed-form coherent DOA estimation. The effectiveness of the proposed method is verified by simulations.
Keyword:
Tensors
Decorrelation
Estimation
Direction-of-arrival estimation
Smoothing methods
Covariance matrices
Matrix decomposition
Coherent DOA estimation
tensor decorrelation
tensorial Hermitian Toeplitz mapping
tensor reconstruction
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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