Iterated Unscented Kalman Particle Filter algorithm based on a variable-step adaptive artificial fish swarm optimization

Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman Particle Filter algorithm, self-adaptive artificial fish swarm algorithm with changing step optimized Iterated Unscented Kalman Particle Filter algorithm was presented in this paper....

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Bibliographic Details
Published in2016 35th Chinese Control Conference (CCC) pp. 7050 - 7053
Main Authors Li, Min, Chen, Zhimei, Shao, Xuejuan
Format Conference Proceeding Journal Article
LanguageEnglish
Published TCCT 01.07.2016
Subjects
a new self-adaptive artificial fish swarm algorithm with changing step
Adaptive algorithms
Algorithms
Alternations
Computer simulation
Conferences
Decision support systems
Fish
Iterated Unscented Kalman Particle Filter algorithm
Optimization
Particle Filter algorithm
Swarming
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Summary:Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman Particle Filter algorithm, self-adaptive artificial fish swarm algorithm with changing step optimized Iterated Unscented Kalman Particle Filter algorithm was presented in this paper. It uses the alternation of behaviors of preying and swarming in the self-adaptive artificial fish swarm algorithm with changing step to optimize the re-sampling process firstly, which makes prior particles move towards the high likelihood region, the problem of sample impoverishment was resolved. As a result, the estimation accuracy of the system state was improved. Simulation results proves the effectiveness of the proposed algorithm.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2016.7554470