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A non-degenerate particle filter under the mutation of nonlinear system state

Year: 2025

Type: conference-paper

Abstract: To address particle degeneracy and sample impoverishment in traditional particle filters, this paper proposes an improved algorithm incorporating Particle Swarm Optimization into the sampling stage. The method selects the maximum-weight particle state as a reference before resampling, then relocates particles toward high-likelihood regions using a PSO-based strategy to enhance diversity and mitigate degeneracy. Furthermore, we introduce and theoretically validate a novel computable proposal distribution. Simulation results demonstrate superior performance over the Unscented Particle Filter (UPF) in both accuracy and computational efficiency, along with robust adaptability to nonlinear system state mutations.

Source: International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB)

DOI: 10.1145/3760269.3760338

Authors: Falin Shen, Ming Wei, Ping Jin, Chengyan Han

Institution: Anhui Sanlian University

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Anhui Sanlian University, Natural Science Foundation of Anhui Province

OpenAlex ID: W4414288572

FWCI:

Cites: 6

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