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Cross-Burg Algorithm for Single-Input Two-Outputs Autoregressive Modeling
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DOI：10.1109/LSP.2021.3101128.png)
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摘要
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This work addresses the Auto-Regressive modeling in Single-Input Two-Outputs (SITO) scenarios, where the lack of input signal diversity prevents application of state-of-the-art multichannel methods. Firstly, we derive a system of Yule-Walker-like equations involving only the cross-correlation of the observations. Then, we leverage the Toeplitz, not Hermitian, structure of the system coefficient matrix to derive an Asymmetric Levinson recursion. Finally, we present a novel lattice based computation of the recursion, named Cross-Burg algorithm. The Cross-Burg lattice is built by two sub-lattices, mutually connected by the reflection coefficients. The Cross-Burg algorithm is inherently robust to uncorrelated additive noise on the two observed channels. Numerical simulations show that the Cross-Burg algorithm outperforms traditional methods in accuracy and noise robustness for SITO-AR modeling and spectral estimation.
Keyword:
Signal processing algorithms
Lattices
Mathematical model
Numerical models
Estimation
Data models
Signal to noise ratio
Single Input Two Outputs AR modeling
Noise robust AR Modeling
Cross-Burg Method
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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