Sparse Least Logarithmic Absolute Difference Algorithm with Correntropy-Induced Metric Penalty

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an l0-norm approximation exerts a zero attraction, and hence, the B Badong Chen [email protected] Wentao Ma [email protected] Haiquan Zhao [email protected] Guan Gui [email protected] Jiandong Duan [email protected] Jose C. Principe [email protected] 1 School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China 2 School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China 3 Department of Electronics and Information Systems, Akita Prefectural University, Yurihonjo 015-0055, Japan 4 Department of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China 5 Department of ECE, University of Florida, Gainesville, FL 32611, USA Circuits Syst Signal Process LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with l1-norm or reweighted l1-norm constraints.