Robust Signal Reconstruction Using the Prolate Spherical Wave Functions and Maximum Correntropy Criterion

TL;DR

This paper introduces a robust signal reconstruction method combining prolate spherical wave functions and maximum correntropy criterion, effectively handling non-Gaussian noise and outliers, and demonstrating improved performance over traditional methods.

Contribution

It presents a novel reconstruction approach that integrates PSWFs with MCC to enhance robustness against impulsive noise and outliers in signal processing.

Findings

Improved mean square error in synthetic signal reconstruction

Enhanced robustness against non-Gaussian noise and outliers

Notable performance gains demonstrated in experiments

Abstract

Signal Reconstruction is one of the most important problem in signal processing. This paper proposes a novel signal reconstruction method based on the prolate spherical wave functions (PSWFs) and maximum correntropy criterion (MCC). The PSWFs are a kind of special functions, which have been proved having good performance in signal reconstruction. However, the existing PSWFs based reconstruction methods only consider the mean square error (MSE) criterion as the cost functions. The MSE criterion is sensitive to the non-Gaussian noise, since it is builded up by the Gaussian assumption. Therefore, for the impulsive noise or outliers, the MSE based reconstruction methods will lead to the large reconstruction error. The proposed MCC and PSWFs based robust signal reconstruction method can reduce the impact of large and non-Gaussian noise. The experimental results on the synthetic signals show that the proposed method can improve the MSE with notable gains in most cases.