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Markovian Adaptive Filtering Algorithm for Block-Sparse System Identification
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DOI：10.1109/TCSII.2021.3069879.png)
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摘要
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In this brief, a novel adaptive filtering algorithm for block-sparse system identification called, Block-Sparse Adaptive Bayesian Algorithm (BS-ABA) is proposed. We use a Gaussian Markov (GM) model to generate the unknown block-sparse system. In the proposed algorithm, a maximum a posteriori (MAP) estimation procedure is used to estimate the adaptive filter coefficients which characterized by a Gaussian Mixture Markov (GMM) model. Moreover, the convergence in the mean of the proposed algorithm is provided in this brief. Simulation results show that the computational complexity of the proposed algorithm is less than some recent algorithms, while the performance is comparable to them.
Keyword:
Markov processes
Estimation
Adaptation models
Adaptive filters
System identification
Adaptive systems
Partitioning algorithms
Adaptive filtering
block-sparse system
maximum a posteriori (MAP)
Gaussian Markov (GM) model
Gaussian Mixture Markov (GMM) model
steepest-ascent
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4.9
论文数:
8.8K
被引数:
2.5W
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