Work

New Bayesian Estimation Method Based on Symmetric Projection Space and Particle Flow Velocity

Year: 2025

Type: article

Abstract: Aiming at the state estimation problem of non-linear systems (NLSs), the traditional typical nonlinear filtering methods (e.g., Particle Filter, PF) have large errors in system state, resulting in low accuracy and high computational speed. To perfect the imperfections, a new Bayesian estimation method based on particle flow velocity (PFV-BEM) is proposed in this paper. Firstly, the state update is carried out according to the projection principle to calculate the prior information of the state and select its particle points. Secondly, the particle flow velocity defined, which describes the evolution process of random samples from the prior distribution to the posterior distribution. The posterior information of the state is calculated by solving the parameters related the particle flow velocity. Finally, the estimated mean and standard deviation of the state are solved. Simulation experiments use one-dimensional general nonlinear examples and multi-target motion tracking as examples, it is compared with PF, the simulation results show the feasibility of the novel Bayesian estimation algorithm.

Source: Symmetry

DOI: 10.3390/sym17060899

Authors: Juan Tan, Zijun Wu, Lijuan Chen

Institutions: Wuhan Technical College of Communications, Wuhan Textile University, Nanjing University of Posts and Telecommunications

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities