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基于ET-PHD的自适应联合跟踪与分类算法

樊鹏飞 李鸿艳

樊鹏飞, 李鸿艳. 基于ET-PHD的自适应联合跟踪与分类算法. 自动化学报, 2019, 45(2): 349-359. doi: 10.16383/j.aas.2018.c170371
引用本文: 樊鹏飞, 李鸿艳. 基于ET-PHD的自适应联合跟踪与分类算法. 自动化学报, 2019, 45(2): 349-359. doi: 10.16383/j.aas.2018.c170371
FAN Peng-Fei, LI Hong-Yan. Adaptive Joint Tracking and Classification Algorithm Using ET-PHD Filter. ACTA AUTOMATICA SINICA, 2019, 45(2): 349-359. doi: 10.16383/j.aas.2018.c170371
Citation: FAN Peng-Fei, LI Hong-Yan. Adaptive Joint Tracking and Classification Algorithm Using ET-PHD Filter. ACTA AUTOMATICA SINICA, 2019, 45(2): 349-359. doi: 10.16383/j.aas.2018.c170371

基于ET-PHD的自适应联合跟踪与分类算法

doi: 10.16383/j.aas.2018.c170371
基金项目: 

陕西省自然科学基础研究计划(2015JM6332)资助

详细信息
    作者简介:

    李鸿艳 空军工程大学信息与导航学院副教授.主要研究方向为信息融合,目标跟踪.E-mail:lihongyan71@sohu.com

    通讯作者:

    樊鹏飞 空军工程大学信息与导航学院硕士研究生.主要研究方向为扩展目标跟踪.本文通信作者.E-mail:13072972016@163.com

Adaptive Joint Tracking and Classification Algorithm Using ET-PHD Filter

Funds: 

Supported by Natural Science Basic Research Plan in Shaanxi Province (2015JM6332)

  • 摘要: 针对新生目标强度先验未知的扩展目标(Extended target,ET)联合跟踪与分类(Joint tracking and classification,JTC)问题,提出一种基于扩展目标概率假设密度(Extended target-probability hypothesis density,ET-PHD)滤波器的自适应联合跟踪与分类算法,并给出其高斯混合实现方法.算法利用量测信息生成新生目标强度,在滤波预测阶段对存活目标和新生目标分别按照其类别进行传播,再引入属性量测信息,用位置和属性的联合量测似然函数代替单目标位置似然函数,对预测后所有目标强度进行联合更新,之后按照类别进行高斯项的删减与合并,提取相应类别目标的状态集.仿真结果表明,提出的自适应算法改进了概率假设密度滤波器在扩展目标跟踪中的性能.
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出版历程
  • 收稿日期:  2017-07-05
  • 修回日期:  2017-11-17
  • 刊出日期:  2019-02-20

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