吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 314-321.doi: 10.13229/j.cnki.jdxbgxb201501046

• 论文 • 上一篇    下一篇

基于平方根容积卡尔曼滤波的目标状态与传感器偏差扩维联合估计算法

刘瑜,何友,王海鹏,董凯   

  1. 海军航空工程学院 信息融合技术研究所,山东 烟台 264001
  • 收稿日期:2013-05-02 出版日期:2015-02-01 发布日期:2015-02-01
  • 作者简介:刘瑜(1986),男,博士研究生.研究方向:多传感器信息融合.E-mail:liuyu77360132@126.com
  • 基金资助:
    国家自然科学基金重点项目(61032001);山东省自然科学基金项目(ZR2012FQ004).

Augmented target tracking algorithm based on SRCKF for joint estimation of state and sensor systematic error

LIU Yu,HE You,WANG Hai-peng,DONG Kai   

  1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001,China
  • Received:2013-05-02 Online:2015-02-01 Published:2015-02-01

摘要: 针对传感器存在系统偏差条件下的三维目标跟踪问题,基于高斯求积规则与三阶球面-径向容积规则,设计了基于平方根容积卡尔曼滤波的目标状态与传感器系统偏差扩维联合估计算法(Augmented state squared-root cubature Kalman filter, ASSRCKF)。仿真分析表明,ASSRCKF不仅避免了扩维扩展卡尔曼滤波算法因模型线性化误差易导致滤波发散的问题,且克服了扩维不敏卡尔曼滤波算法在高维系统中数值不稳定的缺点,算法实时性好,能更加有效地解决带有系统误差的非线性状态估计问题。

关键词: 通信技术, 雷达组网, 传感器系统偏差, 联合状态估计, 容积卡尔曼滤波

Abstract: Considering three dimensional target tracking systems with unknown systematic error, in order to obtain the joint estimation of target state and sensor systematic error, an augmented target tracking algorithm based on Square-root Cubature Kalman Filter (SRCKF) is proposed. The performance of proposed estimation method is analyzed with a numerical example taking account the root mean square error and average computational cost. Simulation results show that the effectiveness of the proposed algorithm is higher than that of the algorithm based on extended Kalman filter in the aspects of estimation accuracy and filtering stability.

Key words: communication, radar networking, sensor systematic error, joint state estimation, cubature Kalman filter

中图分类号: 

  • TN957
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