测绘学报 ›› 2015, Vol. 44 ›› Issue (1): 26-31.doi: 10.11947/j.AGCS.2015.20130286

• 大地测量学与导航 • 上一篇    下一篇

双自适应因子滤波算法

苏天祥1, 文援兰2, 朱俊3   

  1. 1. 61892部队, 广东 汕头 515071;
    2. 国防科技大学航天科学与工程学院, 湖南 长沙 410072;
    3. 宇航动力学国家重点实验室, 陕西 西安 710043
  • 收稿日期:2013-06-04 修回日期:2014-09-28 出版日期:2015-01-20 发布日期:2015-01-22
  • 作者简介:苏天祥(1984-), 男, 硕士, 工程师, 研究方向为卫星导航定位工程. E-mail: tianxiang_su@163.com

The Algorithm of the Dual Adaptive Factors Filtering

SU Tianxiang1, WEN Yuanlan2, ZHU Jun3   

  1. 1. Troop 61892, Shantou 515071, China;
    2. School of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410072, China;
    3. State Key Lab of Astronautic Dynamics, Xi'an 710043, China
  • Received:2013-06-04 Revised:2014-09-28 Online:2015-01-20 Published:2015-01-22

摘要: 抗差自适应滤波算法先求解状态参数抗差解,然后根据抗差解求出的自适应因子来调节动力学模型误差对状态估计的影响.本文针对模型信息不精确和存在观测粗差的情况,提出双自适应因子滤波的思想,采用两个自适应因子分别调节动力学模型信息不精确和观测模型误差对滤波估计的影响,推导出双自适应因子滤波公式,并参考单因子计算方法给出双因子计算公式,最后通过仿真试验比较了双自适应因子滤波算法和抗差自适应滤波算法.仿真结果表明,针对观测粗差,此算法基本能够达到正常观测所得到的状态估值.对于动力学模型短时间内出现的小范围异常误差,此算法可在一定程度上削弱模型不精确对估值的影响.

关键词: 动力学模型信息自适应因子, 观测自适应因子, 均方误差

Abstract: The robust adaptive algorithm solves the status robust solution first, and calculates the adaptive factor by previous solution to adjust the dynamic model error. This article brings the ideas of dual adaptive factors, which the influence of different errors on the state estimations is adjusted by a respective adaptive factor in the case of both model information imprecise and observation gross. It derives the filter formula of the algorithm of the dual adaptive factors filtering, and provides the method of computing dual factors by referring single factors. Finally, it is compared between dual adaptive factors algorithm and robust adaptive algorithm by simulation, the results show that the dual adaptive factors algorithm can basically reach the normal state estimates obtained by normal observation in case of the observation gross errors; and it is to some extent waken the impact of the model inaccurate in the condition of the kinetic model for small-scale short time exception error occurring.

Key words: the dynamical adaptive factor, the observation adaptive factor, root-mean-square error

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