2015/03/03 by Yong Feng, Rui Zeng, Feng, Yong +3
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Electrical engineering #Speech and Audio Processing #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1503.01484
openalex publication_date 2015/03/03 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01
In this paper, we propose a novel leaky least mean square (leaky LMS, LLMS) algorithm which employs a p-norm-like constraint to force the solution to be sparse in the application of system identification. As an extension of the LMS algorithm which is the most widely-used adaptive filtering technique, the LLMS algorithm has been proposed for decades, due to the deteriorated performance of the standard LMS algorithm with highly correlated input. However, both ofthem do not consider the sparsity information to have better behaviors. As a sparse-aware modification of the LLMS, our proposed Lplike-LLMS algorithm, incorporates a p-norm-like penalty into the cost function of the LLMS to obtain a shrinkage in the weight update, which then enhances the performance in sparse system identification settings. The simulation results show that the proposed algorithm improves the performance of the filter in sparse system settings in the presence of noisy input signals.