Weighted diffusion LMP algorithm for distributed estimation in non-uniform noise conditions

2016/08/06 by Hadi Zayyani, Zayyani, H., Mehdi Korki +1
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (stat.ML) #Speech and Audio Processing

paper · pdf · doi:10.48550/arxiv.1608.02060

openalex publication_date 2016/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This letter presents an improved version of diffusion least mean ppower (LMP) algorithm for distributed estimation. Instead of sum of mean square errors, a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors. The weight coefficients are updated by a simple steepest-descent recursion to minimize the error signal of the global and local adaptive algorithm. Simulation results show the advantages of the proposed weighted diffusion LMP over the diffusion LMP algorithm specially in the non-uniform noise conditions in a sensor network.

Related

• Analysis of a Reduced-Communication Diffusion LMS Algorithm
• Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear\n Distributed Networks
• Distributed adaptive algorithm based on the asymmetric cost of error functions
• Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
• Diffusion multi-rate LMS algorithm for acoustic sensor networks