北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (3): 462-469.doi: 10.13700/j.bh.1001-5965.2017.0150

• 论文 • 上一篇    下一篇

多星对合作目标的分布式协同导航滤波算法

李兆铭1, 杨文革2, 丁丹2, 廖育荣2   

  1. 1. 装备学院研究生院, 北京 101416;
    2. 装备学院光电装备系, 北京 101416
  • 收稿日期:2017-03-14 出版日期:2018-03-20 发布日期:2017-09-18
  • 通讯作者: 杨文革 E-mail:wengeyang_3@163.com
  • 作者简介:李兆铭,男,博士研究生。主要研究方向:航天测控最优状态估计;杨文革,男,博士,教授,博士生导师。主要研究方向:先进数字信号处理技术;丁丹,男,博士,讲师。主要研究方向:先进数字信号处理技术;廖育荣,男,硕士,教授。主要研究方向:军事航天技术。
  • 基金资助:
    国家“863”计划(2015AA7026085)

Distributed coordinated navigation filtering algorithm for cooperative target by multi-satellite

LI Zhaoming1, YANG Wenge2, DING Dan2, LIAO Yurong2   

  1. 1. Graduate School, Academy of Equipment, Beijing 101416, China;
    2. Department of Optical and Electrical Equipment, Academy of Equipment, Beijing 101416, China
  • Received:2017-03-14 Online:2018-03-20 Published:2017-09-18
  • Supported by:
    National High-tech Research and Development Program of China (2015 AA7026085)

摘要: 针对多颗在轨卫星对空间合作目标的协同导航问题,提出了一种适用于协同导航的分布式球面单形-径向容积求积分卡尔曼滤波(DSSRCQKF)算法。为了计算非线性滤波中的高斯加权积分,分别使用球面单形准则和二阶高斯-拉盖尔求积分准则计算球面积分和径向积分,提出了一种新的球面单形-径向容积求积分准则。将该准则嵌入分布式卡尔曼滤波框架中,结合协同导航的非线性数学模型,给出适用于协同导航的DSSRCQKF算法,该算法要求每颗导航星仅与其邻居星进行通信,通过数据的分布式融合实现对目标星轨道状态的一致估计,从而避免了传统集中式处理中较高的通信和计算压力。仿真实验结果表明,与分布式卡尔曼滤波相比,本文算法将对合作目标的实时定位精度提高了11 m,定速精度提高了0.02 m/s,从而验证了本文算法的有效性。

关键词: 分布式协同导航, 容积卡尔曼滤波, 球面单形, 高斯-拉盖尔求积分, 非线性系统

Abstract: A distributed spherical simplex-radial cubature quadrature Kalman filter (DSSRCQKF) was proposed aiming at the coordinated navigation problem for cooperative target by multi-satellite on orbit. The spherical simplex rule and second-order Gauss-Laguerre quadrature rule were adopted to calculate the spherical integral and radial integral, respectively, in order to calculate the Gaussian weighted integral in nonlinear Kalman filter, and a novel spherical simplex-radial cubature quadrature rule was put forward. Combined with the nonlinear cooperative navigation mathematical model, the above rule is embedded into the distributed Kalman filter framework to achieve the DSSRCQKF, in which the satellite only needs to communicate with its neighbors. The consensus estimation of the orbital state of the target satellite is achieved using the distributed data fusion method, thus avoiding the higher communication and computational burden in centralized filter. The simulation results show that, compared with the distributed extended Kalman filter, DSSRCQKF improves the real-time positioning accuracy by 11 m and the velocity accuracy by 0.02 m/s, which verifies the validity of the proposed algorithm.

Key words: distributed coordinated navigation, cubature Kalman filter, spherical simplex, Gauss-Laguerre quadrature, nonlinear system

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