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基于STF和加权改进的群目标跟踪算法

李振兴 刘进忙 白东颖 郭相科

李振兴, 刘进忙, 白东颖, 等 . 基于STF和加权改进的群目标跟踪算法[J]. 北京航空航天大学学报, 2014, 40(8): 1102-1108. doi: 10.13700/j.bh.1001-5965.2013.0650
引用本文: 李振兴, 刘进忙, 白东颖, 等 . 基于STF和加权改进的群目标跟踪算法[J]. 北京航空航天大学学报, 2014, 40(8): 1102-1108. doi: 10.13700/j.bh.1001-5965.2013.0650
Li Zhenxing, Liu Jinmang, Bai Dongying, et al. Group targets tracking algorithm based on strong tracking filter and improved weighted method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(8): 1102-1108. doi: 10.13700/j.bh.1001-5965.2013.0650(in Chinese)
Citation: Li Zhenxing, Liu Jinmang, Bai Dongying, et al. Group targets tracking algorithm based on strong tracking filter and improved weighted method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(8): 1102-1108. doi: 10.13700/j.bh.1001-5965.2013.0650(in Chinese)

基于STF和加权改进的群目标跟踪算法

doi: 10.13700/j.bh.1001-5965.2013.0650
基金项目: 

国家自然科学青年基金资助项目(61102109);航空科学基金资助项目(20120196003);空军工程大学防空反导学院“研究生科技创新基金”资助项目(HX1112)

详细信息
    作者简介:

    李振兴(1988-),男,江西吉安人,博士生,lzxing1988@163.com.

  • 中图分类号: TN95

Group targets tracking algorithm based on strong tracking filter and improved weighted method

  • 摘要: 为了进一步提高群目标交互多模型跟踪算法的估计性能,提出一种改进的群跟踪算法.首先,通过采用模型转换概率的自适应算法,优化模型与目标运动模式的实时匹配.并通过引入强跟踪滤波(STF,Strong Tracking Filter)中的渐消因子,提高机动阶段时的群质心的状态估计精度.其次,分别利用概率加权法和标量加权法完成群质心状态和扩展状态的融合估计.最后在变分贝叶斯滤波的基础上,建立完整的跟踪算法流程.仿真实验结果表明,该方法不仅能够提高群质心状态和扩展状态的估计精度,还能有效降低机动阶段时的峰值误差.

     

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出版历程
  • 收稿日期:  2013-11-14
  • 刊出日期:  2014-08-20

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