The Recursive Gauss-Newton Filter

2011/10/24 by Roaldje Nadjiasngar, Nadjiasngar, Roaldje, Michael Inggs +1
Computer Science · Engineering · #Adaptation and Self-Organizing Systems (nlin.AO) #Blind Source Separation Techniques #FOS: Physical sciences #Fault Detection and Control Systems #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1110.5212

openalex publication_date 2011/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a compact, recursive, non-linear, filter, derived from the Gauss-Newton (GNF), which is an algorithm that is based on weighted least squares and the Newton method of local linearisation. The recursive form (RGNF), which is then adapted to the Levenberg-Maquardt method is applicable to linear / nonlinear of process state models, coupled with the linear / nonlinear observation schemes. Simulation studies have demonstrated the robustness of the RGNF, and a large reduction in the amount of computational memory required, identified in the past as a major limitation on the use of the GNF.

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