A Novel Particle Filter Based on One-Step Smoothing for Nonlinear System with Missing Measurements

This paper proposes a novel particle filter based on one-step smoothing for nonlinear systems with missing measurements. This filter iteratively employs a one-step smoother to improve the efficiency of importance sampling in bootstrap particle filtering through incorporating current measurement info...

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Bibliographic Details
Published in2024 3rd Conference on Fully Actuated System Theory and Applications (FASTA) pp. 792 - 797
Main Authors Yang, Zhenrong, Zhang, Xing, Xiao, Yushan
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.05.2024
Subjects
Accuracy
Atmospheric measurements
Current measurement
Missing Measurement
Monte Carlo methods
Nonlinear Filter
One-Step Particle Smoother
Particle Filter
Particle measurements
Sequential Importance Sampling
Smoothing methods
Target tracking
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Summary:This paper proposes a novel particle filter based on one-step smoothing for nonlinear systems with missing measurements. This filter iteratively employs a one-step smoother to improve the efficiency of importance sampling in bootstrap particle filtering through incorporating current measurement information into the apriori distribution. A simulation example of a target tracking model is given to illustrate that the proposed algorithm improves the sampling efficiency and estimation accuracy, which effectively limits the particle degradation phenomenon.
DOI:10.1109/FASTA61401.2024.10595162