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Constrained maximum correntropy criterion based sparse algorithm for sparse channel estimation against noisy input
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DOI：10.1007/s11760-025-04704-5.png)
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Abstract
En 中文
This work proposes bias compensated sparsity aware linear constrained normalized maximum correntropy criterion (CNMCC) based adaptive filtering algorithms. The proposed algorithms are developed by first integrating & ell;1-norm based sparse penalties into linear constrained normalized maximum correntropy criterion adaptive algorithm and later a bias compensator to reduce the bias generated by input noise. The sparse penalties based on & ell;1-norm result into two algorithms, zero attracted CNMCC (ZA-CNMCC) and reweighted zero attracted CNMCC (RZA-CNMCC). The zero attractor accelerates the convergence of sparse coefficients. Further, the bias compensator is added in ZA-CNMCC and RZA-CNMCC algorithms to mitigate the negative effect of input noise by making estimation unbiased. The usefulness of proposed bias compensated ZA-CNMCC (BC-ZA-CNMCC) and bias compensated RZA-CNMCC (BC-RZA-CNMCC) algorithms are demonstrated by experiments carried out in MATLAB software for linear constrained sparse channel estimation application in the presence of impulsive observation noise against noisy input.
Keywords:
Maximum correntropy criterion
Constraint adaptive filtering
Impulsive noise
Sparsity
Bias compensator
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877
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