Proportionate Adaptive Filtering under Correntropy Criterion in Impulsive Noise Environments
For signal processing applications requiring robust adaptive filtering under impulsive noise and variable system sparsity, this work offers an incremental improvement in convergence speed and efficiency.
The authors propose an improved proportionate adaptive filter (IP-MCC) for system identification in impulsive noise environments, achieving faster convergence with similar steady-state error to MCC and lower computational cost.
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.