A Recursive Least P-Order Algorithm Based on M-Estimation in a Non-Gaussian Environment

In recent years, adaptive filtering algorithms based on M-estimation can effectively handle non-Gaussian noise, but large outliers still exist which will seriously disrupt the performance of the algorithm. Here, the recursive least p-Order algorithm based on M-estimation (MRLP) is proposed. The MRLP combines the advantages of M-estimation and the
$l_{p}$
norms. The proposed MRLP can mitigate the impact of large outliers and exhibit strong robustness when suffering from non-Gaussian noise, and can also be well applied to tracking signals. Numerical simulations verify the excellent performance of the proposed algorithm.