适用于点群共存场景的TPMBM跟踪算法
TPMBM tracking algorithm suitable for point-group coexistence scenarios
张双武 1李翠芸 1赵竞哲 2衡博文1
作者信息
- 1. 西安电子科技大学电子工程学院,陕西西安 710071
- 2. 中国电子科技集团公司第二十七研究所,河南郑州 450047
- 折叠
摘要
针对传统群目标跟踪算法在点群共存场景下跟踪精度低的问题,提出了可以同时对点目标和群目标进行跟踪的轨迹泊松多伯努利混合(trajectory Poisson multi-Bernoulli mixture,TPMBM)滤波算法.该算法对目标的状态空间进行扩展,在标准点目标和群目标模型的基础上引入关于目标类别的概率信息,通过TPMBM滤波器的预测和更新过程实现对目标类别的判断和对目标运动状态的估计.仿真结果表明,与现有算法相比,所提算法在点目标和群目标共存时漏检误差明显降低,具有更优的跟踪性能.
Abstract
In order to solve the problem of low tracking accuracy of the traditional group target tracking algorithms in point-group coexistence scenarios,a trajectory Poisson multi-Bernoulli mixture(TPMBM)filte-ring algorithm is proposed,which can track both point target and group target simultaneously.The algorithm expands the state space of the target,introduces probability information about the target class based on the standard point target and the group target models,and achieves the judgment of the target class and the estimation of the target motion state through the prediction and update process of the TPMBM filter.Simulation results show that,compared with the existing algorithms,the proposed algorithm has significantly lower miss detection error and better tracking performance when point target and group target coexist.
关键词
目标跟踪/点群共存/轨迹泊松多伯努利混合滤波Key words
target tracking/point-group coexistence/trajectory Poisson multi-Bernoulli mixture(TPMBM)filtering引用本文复制引用
出版年
2024
NETL
NSTL
万方数据