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Linear System Identification Based on a Third-Order Tensor Decomposition
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DOI：10.1109/LSP.2023.3271185.png)
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摘要
En 中文
A wide variety of system identification problems can be efficiently addressed based on the Kronecker product decomposition of the impulse response, together with low-rank approximations. Such an approach solves the original system identification problem using a combination of two shorter filters. In this letter, targeting a higher dimensionality reduction, we develop a solution based on a third-order tensor decomposition. In addition, the problem of approximating the rank of a tensor is avoided thanks to the control of a matrix rank. Then, an iterative Wiener filter is developed, which outperforms both the conventional benchmark and the previously developed counterpart that exploits the second-order decomposition.
Keyword:
Tensors
Matrix decomposition
Iterative methods
Signal to noise ratio
Linear systems
Jacobian matrices
Indexes
System identification
optimal filtering
tensor decomposition
Kronecker product
Wiener filter
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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