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交互式多模型粒子滤波优化重采样算法

周卫东 孙天 储敏 崔艳青

周卫东, 孙天, 储敏, 等 . 交互式多模型粒子滤波优化重采样算法[J]. 北京航空航天大学学报, 2017, 43(5): 865-871. doi: 10.13700/j.bh.1001-5965.2016.0348
引用本文: 周卫东, 孙天, 储敏, 等 . 交互式多模型粒子滤波优化重采样算法[J]. 北京航空航天大学学报, 2017, 43(5): 865-871. doi: 10.13700/j.bh.1001-5965.2016.0348
ZHOU Weidong, SUN Tian, CHU Min, et al. Interacting multiple model particle filter optimization resampling algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(5): 865-871. doi: 10.13700/j.bh.1001-5965.2016.0348(in Chinese)
Citation: ZHOU Weidong, SUN Tian, CHU Min, et al. Interacting multiple model particle filter optimization resampling algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(5): 865-871. doi: 10.13700/j.bh.1001-5965.2016.0348(in Chinese)

交互式多模型粒子滤波优化重采样算法

doi: 10.13700/j.bh.1001-5965.2016.0348
基金项目: 

国家自然科学基金 61573112

详细信息
    作者简介:

    周卫东, 男, 博士, 教授, 博士生导师。主要研究方向:组合导航、数据融合技术

    孙天, 男, 硕士研究生。主要研究方向:组合导航、数据融合技术

    通讯作者:

    孙天, E-mail:suntian@hrbeu.edu.cn

  • 中图分类号: TN957.52

Interacting multiple model particle filter optimization resampling algorithm

Funds: 

National Natural Science Foundation of China 61573112

More Information
  • 摘要:

    针对标准交互式多模型粒子滤波(IMMPF)算法中存在粒子退化及多样性匮乏问题,提出了交互式多模型粒子滤波优化重采样(IMMPFOR)算法,利用线性优化理论改善模型中具有小权值的粒子精度。该算法的新颖性体现在给定量测信息条件下,利用线性优化方法及模型交互概率将每个模型中拥有小权值的粒子替换成新的粒子。新的粒子既包含本模型中粒子信息,又包含了本模型与其他模型交互后的粒子信息。目标跟踪的仿真结果证明:每个模型新产生的粒子集合可以准确地近似真实状态后验概率密度函数,系统的估计精度与标准IMMPF算法相比有较大提升。

     

  • 图 1  机动目标真实轨迹和估计轨迹

    Figure 1.  True and estimated trajectory of maneuvering target

    图 2  位置和速度均方根误差曲线

    Figure 2.  Curves of root mean square error in position and velocity

    图 3  x方向和y方向位置均方根误差曲线

    Figure 3.  Curves of root mean square error in x and y position

    图 4  x方向和y方向速度均方根误差曲线

    Figure 4.  Curves of root mean square error in x and y velocity

    图 5  N=2 000时位置和速度均方根误差曲线

    Figure 5.  Curves of root mean square error in position and velocity at N=2 000

    表  1  位置均方根误差峰值和均值(N=500)

    Table  1.   Peak and average of root mean square error in distance (N=500)

    算法 RMSE/m
    峰值 均值
    IMMPF 910.1 420.7
    IMMPFOR 552.4 312.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-28
  • 录用日期:  2016-07-07
  • 网络出版日期:  2017-05-20

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