摘要
针对标准标签多伯努利(labeled multi-Bernoulli,LMB)算法只考虑了单个运动模型的问题,提出了一种适用于跳转马尔科夫系统的多模型标签多伯努利(multiple model LMB,MM-LMB)算法。首先对目标状态进行扩展,将多模型思想引入LMB算法得到了新的预测和更新方程,并给出了算法的序贯蒙特卡罗实现。仿真实验表明,MM-LMB算法能对多机动目标进行有效跟踪,在复杂探测环境下跟踪精度优于多模型概率假设密度(multiple model probability hypothesis density,MM-PHD)算法和多模型势平衡多目标多伯努利(multiple model cardinality balanced multi-target multi-Bernoulli,MM-CBMeMBer)算法;所提算法计算量当目标相距较远时低于MM-PHD和MM-CBMeMBer,目标聚集时增长速度快于对比算法。
For the problem that the standard labeled multi-Bernoulli （LMB） filter only considers the single motion model case, a multiple model LMB （MM-LMB） filter for maneuvering target tracking is proposed. By introducing the jump Markov （JM） system to the LMB method, the extended recursion formulations are presen- ted, and the sequential Monte Carlo implementation of the proposed method is given. Simulations show that the MM-LMB filter can track multiple maneuvering targets effectively, and has higher tracking accuracy than the multiple model probability hypothesis density （MM-PHD） filter and the multiple model cardinality balanced multi-target multi-Bernoulli （MM-CBMeMBer） filter in complex detection environment. The calculation cost of the proposed method is lower than MM-PHD and MM-CBMeMBer when the targets are not closed, while grows faster than the compared algorithms when the targets gather together.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2015年第12期2683-2688,共6页
Systems Engineering and Electronics
基金
国家高技术研究发展计划(863计划)(2014AAXXX4061)资助课题
关键词
多目标跟踪
机动目标
标签多伯努利
序贯蒙特卡罗
multi-target tracking
maneuvering target
labeled multi-Bernoulli （LMB）
sequential Monte Carlo
