An Improved Robust Total Logistic Distance Metric algorithm for Generalized Gaussian Noise and Noisy Input

TL;DR

This paper introduces the TACLDM algorithm, which improves robustness and reduces parameter tuning in generalized Gaussian noise environments, outperforming existing methods in steady-state error and stability.

Contribution

The paper proposes the TACLDM algorithm that uses logical distance metric and arctangent function to enhance robustness and reduce parameters compared to prior algorithms.

Findings

TACLDM has fewer parameters than competing algorithms.

TACLDM demonstrates superior robustness to generalized Gaussian noise.

Theoretical analysis confirms the algorithm's effectiveness.

Abstract

Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual ad-justment of parameters is excessive, greatly increasing the practical difficulty of use. To solve this problem, the total arctangent based on logical distance metric (TACLDM) algo-rithm is proposed by utilizing the advantage of few parameters in logical distance metric (LDM) theory and the convergence behavior is improved by the arctangent function. Compared with other competing algorithms, the TACLDM algorithm not only has fewer parameters, but also has better robustness to generalized Gaussian noise and significantly reduces the steady-state error. Furthermore, the analysis of the algorithm in the generalized Gaussian noise environment is analyzed in detail in this paper. Finally, computer simulations demonstrate the outstanding performance of the TACLDM algorithm and the rigorous theoretical deduction in this paper.