Simulation and Performance Analysis of Adaptive Filtering Algorithms in Noise Cancellation

TL;DR

This paper compares the performance of RLS, FTRLS, and GAL adaptive filtering algorithms in noise cancellation, showing GAL outperforms others in correlation, SNR, and convergence time.

Contribution

It provides the first comparative analysis of RLS, FTRLS, and GAL algorithms for noise cancellation based on multiple performance metrics.

Findings

GAL performs best in noise cancellation metrics.

RLS, FTRLS, and GAL were evaluated and compared for the first time.

GAL shows superior convergence and noise reduction performance.

Abstract

Noise problems in signals have gained huge attention due to the need of noise-free output signal in numerous communication systems. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper concentrates upon the analysis of adaptive noise canceller using Recursive Least Square (RLS), Fast Transversal Recursive Least Square (FTRLS) and Gradient Adaptive Lattice (GAL) algorithms. The performance analysis of the algorithms is done based on convergence behavior, convergence time, correlation coefficients and signal to noise ratio. After comparing all the simulated results we observed that GAL performs the best in noise cancellation in terms of Correlation Coefficient, SNR and Convergence Time. RLS, FTRLS and GAL were never evaluated and compared before on their performance in noise cancellation in terms of the criteria we considered here.