引用本文:冯新喜,迟珞珈,王泉,等.一种改进的广义标签多伯努利机动扩展目标跟踪算法[J].控制与决策,2019,34(10):2143-2149
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一种改进的广义标签多伯努利机动扩展目标跟踪算法
冯新喜,迟珞珈,王泉,蒲磊
(空军工程大学信息与导航学院,西安710077)
摘要:
针对广义标签多伯努利滤波器(GLMB)预测步和更新步分别需要进行剪枝而导致计算量大、运行效率低且只考虑到单个运动模型的问题,提出一种多模型一步更新广义标签多伯努利机动扩展目标跟踪算法.首先通过公式推导将预测步与更新步合并,给出一种新的一步递归表达式;然后将多模型思想引入到一步递归表达式中,得到最终的多模型一步更新方程,同时基于吉布斯采样提出一种快速剪枝方法对其进行剪枝.由于改进后的滤波算法只涉及到一次剪枝且剪枝方法高效,算法的运行时间大大缩短;同时,由于采用了多模型思想,对机动目标的跟踪精度有了一定的提高.仿真实验表明,所提出的改进算法可以有效估计机动目标状态,且相比于多模型标签多伯努利滤波器(MMGLMB)计算效率明显提高.
关键词:  随机有限集  标签多伯努利  多扩展目标跟踪  机动目标
DOI:10.13195/j.kzyjc.2018.0126
分类号:TN953
基金项目:国家自然科学基金项目(61571458).
An improved generalized labeled multi-Bernoulli filter for maneuvering extended target tracking
FENG Xin-xi,CHI Luo-jia,WANG Quan,PU Lei
(Information and Navigation College,Air Force Engineering University,Xián 710077,China)
Abstract:
In view of the problems of large amount of calculation and low efficiency resulted by pruning of generalized labeled multi-Bernoulli(GLMB) prediction and updating steps, as well as deficiency of only taking single motion model into consideration, a model of multiple model one step updating GLMB maneuvering extended target tracking algorithm is proposed. Firstly, by merging prediction step with updating step through formula deduction, a new step recursion expression is proposed. Then, the multiple model algorithm is introduced to one step recursion expression to obtain the final step of update equation, and a quick pruning method is proposed based on Gibbs for sampling. Since the improved algorithm only involves one pruning and the pruning method is efficient, the operation time of the algorithm is greatly shortened. At the same time, the multiple model is used to improve the tracking accuracy of the maneuvering target. Simulation results show that the proposed algorithm can effectively estimate the state of the maneuvering target, and the computational efficiency is significantly improved compared to the MMGLMB filter.
Key words:  random finite set  labeled multi-Bernoulli  multi-extended target tracking  maneuvering target

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