A hybrid labeled multi-Bernoulli tracking algorithm based on box particle filter

被引：0
|
作者：
Feng X.-X. [1 ]
Chi L.-J. [1 ]
Wang Q. [1 ]
Miao L. [1 ]
机构：
[1] Information and Navigation College, Air Force Engineering University, Xi'an
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 02期
关键词：
Entropy; Generalized labeled multi-Bernoulli; Interval analysis; Kullback Leibler divergence; Labeled multi-Bernoulli; Random finite set;
D O I：
10.13195/j.kzyjc.2018.0652
中图分类号：
学科分类号：
摘要：
In view of the problems that the labeled multi-Bernoulli has a decline tracking effect when the targets are close or track-to-measurement association is ambiguous because of the approximate information loss in update step, the interval analysis technology is introduced. Combined with respective advantages of the generalized labeled multi-Bernoulli (GLMB) and labeled multi-Bernoulli (LMB), a hybrid labeled multi-Bernoulli tracking algorithm based on box particle filter (Box-HLMB) is proposed. The GLMB and LMB parameter sets are established. By switch between the GLMB and LMB based on the Kullback Leibler divergence and entropy evaluation criteria, the GLMB is used in the critical environment to improve the tracking performance, LMB approximation is used in other environment to improve the efficiency of operation. The hybrid labeled multi-Bernoulli algorithm is implemented based on box particle filter. The simulation results show that compared with the GLMB and LMB filtering algorithms, the improved algorithm can ensure the computational efficiency, as well as improving the accuracy and stability of the tracking performance. © 2020, Editorial Office of Control and Decision. All right reserved.
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页码：507 / 512
页数：5
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