Low Complexity Design of Logistic Distance Metric Adaptive Filter for Impulsive Noise Environments
Low Complexity Design of Logistic Distance Metric Adaptive Filter for Impulsive Noise Environments
Abstract:
In many practical scenarios, non-Gaussian noise contaminates the desired signal and introduces outliers. The recently proposed logistic distance metric adaptive filter (LDMAF) outperforms the existing algorithms and provides better performance in the presence of such outliers. There is a need for efficient hardware architecture for the implementation of LDMAF. This article proposes an efficient VLSI architecture of LDMAF. The implementation of error-gradient function of LDMAF puts significant implementation problem in terms of delay and cost. We introduce here an efficient tangent-based piecewise linear (TPL) approximation algorithm for implementing the corresponding architecture. The proposed approach improves the power, performance, and area (PPA) metrics over state-of-the-art implementations of other robust algorithms while meeting system performance within an acceptable deviation.
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Low Complexity Design of Logistic Distance Metric Adaptive Filter for Impulsive Noise Environments
