计算机应用 ›› 2015, Vol. 35 ›› Issue (5): 1353-1357.DOI: 10.11772/j.issn.1001-9081.2015.05.1353

• 人工智能 • 上一篇    下一篇

基于距离参数化的混合坐标系下平方根容积卡尔曼滤波纯方位目标跟踪

周德云, 章豪, 张堃, 张凯, 潘潜   

  1. 西北工业大学 电子信息学院, 西安 710129
  • 收稿日期:2014-12-11 修回日期:2015-01-07 出版日期:2015-05-10 发布日期:2015-05-14
  • 通讯作者: 章豪
  • 作者简介:周德云(1964-),男,浙江义乌人,教授,博士生导师,主要研究方向:先进火力控制; 章豪(1992-),男,安徽滁州人,硕士研究生,主要研究方向:目标跟踪; 张堃(1982-),男,陕西西安人,讲师,博士, 主要研究方向:先进控制.
  • 基金资助:

    国家自然科学基金资助项目(61401363).

Range-parameterized square root cubature Kalman filter using hybrid coordinates for bearings-only target tracking

ZHOU Deyun, ZHANG Hao, ZHANG Kun, ZHANG Kai, PAN Qian   

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an Shaanxi 710129, China
  • Received:2014-12-11 Revised:2015-01-07 Online:2015-05-10 Published:2015-05-14

摘要:

针对纯方位单站目标跟踪中观测方程非线性且易受滤波初值影响的问题,提出了一种距离参数化混合坐标系下的平方根容积卡尔曼滤波(SRCKF)算法.该滤波算法首先将平方根容积卡尔曼滤波算法应用于混合坐标系,比直角坐标系下的平方根容积卡尔曼滤波算法能得到更好的跟踪效果;接着将距离参数化思想和混合坐标系下的平方根容积卡尔曼滤波算法结合,消除了距离信息不可测对跟踪效果的影响.仿真结果表明,该滤波算法虽略微提升了计算复杂度,但其鲁棒性和滤波精度均有大幅度的提高.

关键词: 纯方位跟踪, 混合坐标系, 距离参数化, 平方根容积卡尔曼滤波, 鲁棒性

Abstract:

In order to solve the problems of having nonlinear observation equations and being susceptible to initial value of filtering in bearings-only target tracking, a range-parameterized hybrid coordinates Square Root Cubature Kalman Filter (SRCKF) algorithm was proposed. Firstly,it applied the SRCKF to hybrid coordinates,obtained better tracking effect than the SRCKF under Cartesian coordinates. And then it combined the range parameterization strategy with the SRCKF under hybrid coordinates, and eliminated the impact of unobservable range. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness although the computational complexity increases slightly.

Key words: bearings-only tracking, hybrid coordinates, range parameterization, Square Root Cubature Kalman Filter (SRCKF), robustness

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