A Particle Filter Algorithm Based on Mixing of Prior Probability Density and UKF as Generate Importance Function

2015 ◽  
Vol 743 ◽  
pp. 403-406
Author(s):  
Liu Lu ◽  
J. Wang ◽  
M. Yang ◽  
S. Geng
Keyword(s):  
Particle Filter ◽  
Prior Probability ◽  
Negative Influence ◽  
Nonlinear Filter ◽  
Comprehensive Method ◽  
Importance Function ◽  
Particle Filter Algorithm ◽  
Technical Features ◽  
Key Steps ◽  
Degeneracy Problem

As an important nonlinear filter theory, particle filter is a heated issue in domestic and foreign researches. The option of importance density is one of the key steps of particle filter algorithm. The proper option of importance density can minish the negative influence of filter algorithm caused by degeneracy problem. This paper introduces several widely-used options of importance density systemically, and analyzes their features and applied perspectives respectively. The paper also advances a comprehensive method of importance density, analyzes its technical features, explores the adjudgement and improvement of this method based on various performance, and finally puts forward the necessary further study according to the engineer requirements.

Wave Filtering of Ship Dynamic Positioning System Using Particle Filter

2013 ◽  
Vol 397-400 ◽  
pp. 551-555
Author(s):  
Wen Juan Li ◽  
Hai Xiang Xu ◽  
Hui Feng
Keyword(s):  
Particle Filter ◽  
Low Frequency ◽  
Nonlinear Filter ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Dynamic Positioning System ◽  
Frequency Position ◽  
Particle Filter Algorithm

This paper presents a nonlinear filter which is particle filter. The filter produces accurate estimates of low-frequency position and velocity only from measured values of ship position and heading in Dynamic Positioning System. The results of simulation confirm the validity and adaptability of the particle filter algorithm.

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

A quick evaluation method for the lifetime of the fuel cell MEA with the particle filter algorithm

2021 ◽  
pp. 1-14
Author(s):  
Luyan He ◽  
Zhigang Zhan ◽  
Hong Chen ◽  
Panxing Jiang ◽  
Yuan Yu ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Particle Filter ◽  
Evaluation Method ◽  
Particle Filter Algorithm
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