Proportionate Adaptive Filtering under Correntropy Criterion in Impulsive Noise Environments

2017/07/02 by Vinay Chakravarthi Gogineni, Gogineni, Vinay Chakravarthi, Subrahmanyam Mula +1
Engineering · Computer Science · #Advanced Adaptive Filtering Techniques #Speech and Audio Processing #Blind Source Separation Techniques

paper · pdf · doi:10.48550/arxiv.1707.00315

Abstract

An improved proportionate adaptive filter based on the Maximum Correntropy Criterion (IP-MCC) is proposed for identifying the system with variable sparsity in an impulsive noise environment. Utilization of MCC mitigates the effect of impulse noise while the improved proportionate concepts exploit the underlying system sparsity to improve the convergence rate. Performance analysis of the proposed IP-MCC is carried out in the steady state and our analysis reveals that the steady state Excess Mean Square Error (EMSE) of the proposed IP-MCC filter is similar to the MCC filter. The proposed IP-MCC algorithm outperforms the state of the art algorithms and requires much less computational effort. The claims made are validated through exhaustive simulation studies using the correlated input.

Citations

• Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm

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