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Enhanced Diffusion Recursive Least Squares Approach Utilizing Triangular Matrix Factorization for Distributed Systems
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Keywords
Diffusion recursive least squares
triangular matrix factorization
distributed systems
adaptive networks
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Abstract
Distributed estimation and adaptive filtering play a pivotal role in modern large-scale networked systems, enabling robust signal processing under dynamic and uncertain environments. This study introduces an enhanced Diffusion Recursive Least Squares (D-RLS) framework leveraging triangular matrix factorization for distributed systems. The proposed approach integrates the stability and convergence benefits of triangular decomposition into classical D-RLS algorithms, thereby reducing computational complexity and improving network-wide estimation performance. Analytical derivations establish the theoretical foundation, while simulation studies demonstrate the superior convergence rate and resilience to noise and communication constraints. Comparative evaluation against standard diffusion strategies highlights significant improvements in stability and accuracy. The findings suggest that incorporating triangular factorization into distributed adaptive networks can substantially enhance multi-agent learning efficiency and resource utilization.
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