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非线性量测下自适应噪声协方差PHD滤波

袁常顺 王俊 向洪 魏少明 张耀天

袁常顺, 王俊, 向洪, 等 . 非线性量测下自适应噪声协方差PHD滤波[J]. 北京航空航天大学学报, 2017, 43(1): 53-60. doi: 10.13700/j.bh.1001-5965.2016.0034
引用本文: 袁常顺, 王俊, 向洪, 等 . 非线性量测下自适应噪声协方差PHD滤波[J]. 北京航空航天大学学报, 2017, 43(1): 53-60. doi: 10.13700/j.bh.1001-5965.2016.0034
YUAN Changshun, WANG Jun, XIANG Hong, et al. Adaptive noise covariance PHD filter under nonlinear measurement[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(1): 53-60. doi: 10.13700/j.bh.1001-5965.2016.0034(in Chinese)
Citation: YUAN Changshun, WANG Jun, XIANG Hong, et al. Adaptive noise covariance PHD filter under nonlinear measurement[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(1): 53-60. doi: 10.13700/j.bh.1001-5965.2016.0034(in Chinese)

非线性量测下自适应噪声协方差PHD滤波

doi: 10.13700/j.bh.1001-5965.2016.0034
基金项目: 

国家自然科学基金 61471019,61501011,61501012

详细信息
    作者简介:

    袁常顺,男,博士研究生。主要研究方向:雷达信号处理、随机有限集多目标跟踪

    通讯作者:

    王俊,男,博士,教授,博士生导师。主要研究方向:雷达信号处理、实时信号处理。E-mail:wangj203@buaa.edu.cn.

  • 中图分类号: TN957.51

Adaptive noise covariance PHD filter under nonlinear measurement

Funds: 

National Natural Science Foundation of China 61471019,61501011,61501012

More Information
  • 摘要:

    概率假设密度(PHD)滤波算法已被证明是实时多目标跟踪的有效方法,但现有这些基于PHD滤波的方法假设量测噪声协方差先验已知,而实际中量测噪声协方差可能是未知或随着环境改变而变化。针对这一问题,提出了一种适用于非线性量测模型的自适应噪声协方差多目标跟踪算法。该算法以PHD滤波为基础,采用容积卡尔曼(CK)技术近似非线性量测模型,利用逆威沙特(IW)分布描述量测噪声协方差分布,通过变分贝叶斯(VB)近似技术迭代估计量测噪声协方差和多目标状态联合后验密度。仿真结果表明,本文所提算法可有效估计量测噪声协方差,同时实现准确的目标数和目标状态估计。

     

  • 图 1  真实目标运动场景

    Figure 1.  True target movement scene

    图 2  包含杂波目标运动航迹量测结果

    Figure 2.  Track measurement results of target movementimmersed in clutters

    图 3  ACK-VB-PHD滤波算法位置估计

    Figure 3.  Position estimation of ACK-VB-PHD filter algorithm

    图 4  不同算法目标数估计

    Figure 4.  Target number estimation for different algorithms

    图 5  不同算法OSPA距离比较(c=300,p=2)

    Figure 5.  Comparison of OSPA distance for different algorithms(c=300,p=2)

    图 6  时变量测噪声协方差下目标数和协方差估计结果

    Figure 6.  Target number and covariance estimation results fortime-varying measurement noise covariance

    图 7  时变量测噪声协方差OSPA距离比较(c=300,p=2)

    Figure 7.  Comparison of OSPA distance for time-varyingmeasurement noise covariance(c=300,p=2)

    图 8  不同杂波率下OSPA距离比较(c=300,p=1)

    Figure 8.  Comparison of OSPA distance at different clutter rates(c=300,p=1)

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出版历程
  • 收稿日期:  2016-01-08
  • 录用日期:  2016-02-29
  • 刊出日期:  2017-01-20

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