Sparsity-Aware Recursive Maximum Correntropy Criteria Adaptive Filtering Algorithm
نویسندگان
چکیده
To address sparse channel estimation problem in nonGaussian impulsive noise environment, a recursive maximum correntropy criteria (RMCC) algorithm using sparse constraint is proposed to combat impulsive-inducing instability. Specifically, the recursive algorithm on the correntrioy with a forgetting factor of error at iteration is to solve steady-state error for improving the maximum correntropy criteria (MCC) based algorithms. Considering an unknown sparse channel, the simple and efficient zero-attracting is employed in the RMCC algorithm to exploit sparsity as well as to mitigate the impulsive noise simultaneously. Numerical simulations are given to show that the proposed algorithm is robust while providing robust steady-state estimation performance. Keywords—Recursive correnyropy criterion algorithm; l1 norm constraint; sparse parameter estimation; non-Gaussian impulsive noise.
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Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
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