王春平,
陆军工程大学石家庄校区电子与光学工程系 ??石家庄 ??050003
详细信息
作者简介:刘江义：男，1988年生，博士生，研究方向为多目标跟踪、信息融合等
王春平：男，1965年生，教授，研究方向为图像处理、目标跟踪等
通讯作者:王春平 wchp17@139.com
中图分类号:TP391计量
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被引次数:0
出版历程
收稿日期:2018-04-17
修回日期:2018-09-10
网络出版日期:2018-09-25
刊出日期:2019-02-01
Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains
Jiangyi LIU,Chunping WANG,
Electronic and optical engineering Department, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China
摘要
摘要:针对已有的基于双马尔科夫链(PMC)模型的势概率假设密度(PMC-CPHD)滤波算法无法实现的问题，将PMC-CPHD算法改进为多项式形式以便于算法的实现，并给出了改进算法的高斯混合(GM)实现。实验结果表明给出的GM实现能够有效实现多目标跟踪，并且比基于PMC模型的概率假设密度(PMC-PHD)算法的GM实现提高了目标个数估计的稳定性。
关键词:双马尔科夫链/
势概率假设密度/
高斯混合
Abstract:In view of the problem that the Cardinalized Probability Hypothesis Density (CPHD) probability hypothesis density filtering algorithm based on the Pairwise Markov Chains (PMC) model (PMC-CPHD) is not suitable for implementation, the PMC-CPHD algorithm is modified into a polynomial form to facilitate implementation, and the Gauss Mixture (GM) implementation of the improved algorithm is given. The experimental results show that the given GM implementation realizes multitarget tracking effectively, and improves the stability of the target number estimation compared with the GM implementation of the probability hypothesis density filtering algorithm based on the PMC model (PMC-PHD).
Key words:Pairwise Markov Chains (PMC)/
Cardinalized Probability Hypothesis Density (CPHD)/
Gauss Mixture (GM)
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