Target Filter Tracking Algorithm Based on IABBSCA-IMM

Aiming at the problem that the probability transition matrix is set by prior information and the filtering parameters are fixed in the traditional interactive multimodel (IMM) algorithm, which leads to the model probability lag in the switching process and the low filtering and tracking accuracy, an...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2022; pp. 1 - 11
Main Authors Jiao, Shangbin, Du, Jinghang, Li, Yujun, Li, Yuxing
Format Journal Article
LanguageEnglish
Published New York Hindawi 06.07.2022
Hindawi Limited
Subjects
Acceleration
Accuracy
Algorithms
Engineering
Filtration
Mathematical models
Neural networks
Optimization
Parameters
Root-mean-square errors
Tracking
Trigonometric functions
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Summary:Aiming at the problem that the probability transition matrix is set by prior information and the filtering parameters are fixed in the traditional interactive multimodel (IMM) algorithm, which leads to the model probability lag in the switching process and the low filtering and tracking accuracy, an interactive multimodel filter tracking (IABBSCA-IMM) algorithm with improved parameter adaptive and bare bone sine cosine optimization is proposed. First, the Markov probability transition matrix is dynamically adjusted and limited conditions are added through the parameter adaptation method; then, the filtering parameters Q and R are optimized by the bare bones sine cosine algorithm (BBSCA); finally, three motion models of CV (uniform velocity motion), CA (uniform acceleration motion), and CT (uniform velocity turning motion) are used to conduct filtering and tracking experiments on the target. The simulation results show that, compared with the IMM algorithm, the AMP-IMM algorithm, the IASCA-IMM algorithm, and the IABBSCA-IMM algorithm proposed in this study have the smallest position and velocity root mean square error (RMSE) in the X and Y directions, and the accuracy is better.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/8160970