Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise. Issue 3 (March 2016) - Ma, Wentao; Chen, Badong; Qu, Hua; Zhao, Jihong
Abstract The least mean p-power (LMP) is one of the most popular adaptive filtering algorithms. With a properp value, the LMP can outperform the traditional least mean square $$(p=2)$$ ( p = 2 ), especially under the impulsive noise environments. In sparse channel estimation, the unknown channel may have a sparse impulsive (or frequency) response. In this paper, our goal is to develop new LMP algorithms that can adapt to the underlying sparsity and achieve better performance in impulsive noise environments. Particularly, the correntropy induced metric (CIM) as an excellent approximator of the $$l_0$$ l 0 -norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the $$l_1$$ l 1 -norm, reweighted $$l_1$$ l 1 -norm and CIM penalized LMP algorithms, which are denoted as ZALMP, RZALMP and CIMLMP respectively. The mean and mean square convergence of these algorithms are analysed. Simulation results show that the proposed new algorithms perform well in sparse channel estimation under impulsive noise environments. In particular, the CIMLMP with suitable kernel width will outperform other algorithms significantly due to the superiority of the CIM approximator for the $$l_0$$ l 0 -norm.