A Novel Unscented Particle Filter Based on Maximum Fuzzy Correntropy

2022 ◽  
pp. 377-386
Author(s):  
Liang-qun Li ◽  
Ying-chun Sun ◽  
Chu-yun Zhang
Keyword(s):  
Particle Filter ◽  
Unscented Particle Filter

Robust information unscented particle filter based on M‐estimate

2019 ◽  
Vol 13 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Xiao‐Hang Wu ◽  
Shen‐Min Song
Keyword(s):  
Particle Filter ◽  
Unscented Particle Filter ◽  
M Estimate

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.

Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Robotica ◽  
2020 ◽  
pp. 1-14
Author(s):  
Chen Hao ◽  
Liu Chengju ◽  
Chen Qijun
Keyword(s):  
Particle Filter ◽  
Humanoid Robots ◽  
Dynamic Environments ◽  
Measurement Unit ◽  
Dual Channel ◽  
Nao Robot ◽  
Unscented Particle Filter ◽  
Monte Carlo Localization ◽  
Switch Mechanism ◽  
Updating Procedure

SUMMARY Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

2013 ◽  
Vol 53 (6) ◽  
pp. 805-810 ◽  
Author(s):  
Qiang Miao ◽  
Lei Xie ◽  
Hengjuan Cui ◽  
Wei Liang ◽  
Michael Pecht
Keyword(s):  
Particle Filter ◽  
Lithium Ion Battery ◽  
Life Prediction ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Filter Technique ◽  
Unscented Particle Filter ◽  
Useful Life

scholarly journals Memory Unscented Particle Filter for 6-DOF Tactile Localization

2017 ◽  
Vol 33 (5) ◽  
pp. 1139-1155 ◽  
Author(s):  
Giulia Vezzani ◽  
Ugo Pattacini ◽  
Giorgio Battistelli ◽  
Luigi Chisci ◽  
Lorenzo Natale
Keyword(s):  
Particle Filter ◽  
Tactile Localization ◽  
Unscented Particle Filter
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