Abstract
This paper presents a new particle filter algorithm (MultiPDF) for state estimation of nonlinear systems. The proposed method is a modification of the standard particle filter approach. Due to the strong need for the acceleration of calculations and an improvement in the estimation quality of state estimation, the authors propose a method which enables one to divide the main particle filter into smaller sub-filters with an accordingly smaller number of particles for each one of them. The algorithm has been implemented for various numbers of particles and subordinate parallel filters. Estimation quality has been checked for nine nonlinear objects (both one- and multidimensional) and evaluated through the quality index, average root-mean-squared error. The computation time of the particle filter algorithm for several hardware configurations has been compared. Based on the obtained results, it can be concluded that, besides the computation acceleration, the parallelization of the particle filter’s operation also improves the estimation quality.
Highlights
Scientific and technical problems with noisy measurements require state estimation to filter out the noise
We propose a modification of the particle filter—MultiPDF PF, which divides the main filter into
The most interesting results were obtained for versions (v4) and (v5), because for these versions of MultiPDF PF, a lot of parallel filters still improve the estimation quality
Summary
Scientific and technical problems with noisy measurements require state estimation to filter out the noise. An efficient tool for measurement noise filtering is the particle filter (PF), firstly proposed by Gordon, Salmond and Smith in 1993 [10]. The PF algorithm builds on the Bayesian Filter, which is the optimal solution [11]. It enables estimation in the presence of noise with any probability density function (PDF). It provides proper estimates even for strongly nonlinear objects, in contrast to Kalman filter algorithms [12]
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