Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise

TL;DR

This paper introduces a novel particle filtering approach combined with recursive least squares for improved parameter estimation in bilinear systems affected by colored noise, demonstrating superior accuracy and practicality.

Contribution

The paper presents the B-PF-RLS algorithm, integrating particle filtering with RLS, and eliminates the need for explicit noise variance knowledge, advancing system identification methods.

Findings

Superior parameter and state estimation accuracy under uncertain noise conditions

Effective handling of system nonlinearities and colored noise

Enhanced practicality by removing the requirement for noise variance knowledge

Abstract

This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the measurement noise variance, enhancing the method's practicality for real-world applications. Numerical examples demonstrate the B-PF-RLS algorithm's superior performance in accurately estimating both system parameters and states, even under uncertain noise conditions. This work offers a robust and effective solution for system identification in various engineering applications involving bilinear models subject to complex noise environments.