留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

小视场星敏感器量测延时滤波算法

钱华明 王迪 吴永慧 黄智开

钱华明, 王迪, 吴永慧, 等 . 小视场星敏感器量测延时滤波算法[J]. 北京航空航天大学学报, 2019, 45(2): 234-242. doi: 10.13700/j.bh.1001-5965.2018.0279
引用本文: 钱华明, 王迪, 吴永慧, 等 . 小视场星敏感器量测延时滤波算法[J]. 北京航空航天大学学报, 2019, 45(2): 234-242. doi: 10.13700/j.bh.1001-5965.2018.0279
QIAN Huaming, WANG Di, WU Yonghui, et al. Filtering algorithm of NFOV star sensor measurement delay[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 234-242. doi: 10.13700/j.bh.1001-5965.2018.0279(in Chinese)
Citation: QIAN Huaming, WANG Di, WU Yonghui, et al. Filtering algorithm of NFOV star sensor measurement delay[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 234-242. doi: 10.13700/j.bh.1001-5965.2018.0279(in Chinese)

小视场星敏感器量测延时滤波算法

doi: 10.13700/j.bh.1001-5965.2018.0279
基金项目: 

国家自然科学基金 61573113

详细信息
    作者简介:

    钱华明 男, 博士, 教授, 博士生导师。主要研究方向:组合导航、星敏感器、信息处理、传感器技术与智能系统技术

    王迪 女, 硕士研究生。主要研究方向:飞行器姿态算法

    吴永慧 女, 博士研究生。主要研究方向:姿态估计、信息融合

    黄智开 男, 博士研究生。主要研究方向:数字信号处理

    通讯作者:

    钱华明, E-mail: qianhuam@sina.com

  • 中图分类号: U666.12

Filtering algorithm of NFOV star sensor measurement delay

Funds: 

National Natural Science Foundation of China 61573113

More Information
  • 摘要:

    针对小视场(NFOV)星敏感器用于姿态估计时存在的量测延时情况,提出了一种用于解决量测延时的鲁棒扩展卡尔曼滤波(REKF)算法。根据最小方差准则的思想求解各方差的最小上界,通过最小上界确定滤波增益,设计的REKF算法可以有效解决量测延时问题,提高了姿态估计的精度。对REKF算法进行了仿真验证,结果表明:该算法优于常规加性扩展卡尔曼滤波(AEKF)算法、鲁棒有界时域滤波(RFHF)算法及鲁棒卡尔曼滤波(RKF)算法,能较好解决非线性系统存在的量测延时问题,验证了该算法的有效性。

     

  • 图 1  情况1时姿态角均方根误差对比

    Figure 1.  Comparison of root mean square error of attitude angle in Case 1

    图 2  情况1时姿态角误差对比

    Figure 2.  Comparison of attitude angle error in Case 1

    图 3  情况2时姿态角均方根误差对比

    Figure 3.  Comparison of RMSE of attitude angle in Case 2

    图 4  情况2时姿态角误差对比

    Figure 4.  Comparison of attitude angle error in case 2

  • [1] XIONG K, WEI C L, LIU L D.Robust extended Kalman filtering for nonlinear systems with stochastic uncertainties[J].IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2010, 40(2):399-405. doi: 10.1109/TSMCA.2009.2034836
    [2] XIONG K, WEI C L, LIU L D.Robust Kalman filtering for discrete-time nonlinear systems with parameter uncertainties[J].Aerospace Science & Technology, 2012, 18(1):15-24. http://www.sciencedirect.com/science/article/pii/S1270963811000708
    [3] XIONG K, LIU L D, LIU Y.Robust extended Kalman filtering for nonlinear systems with multiplicative noises[J].Optimal Control Applications & Methods, 2011, 32(1):47-63. doi: 10.1002/oca.928/citedby
    [4] REZAEI H, ESFANJANI R M, FARSI M.Robust filtering for uncertain networked systems with randomly delayed and lost measurements[J].IET Signal Processing, 2015, 9(4):320-327. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fa9a615038aae276e7866cef497e25e3
    [5] INOUE R S, TERRA M H, CERRI J P.Extended robust Kalman filter for attitude estimation[J].IET Control Theory & Applications, 2016, 10(2):162-172. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0220708619/
    [6] DONG Z, YOU Z.Finite-horizon robust Kalman filtering for uncertain discrete time-varying systems with uncertain-covariance white noises[J].IEEE Signal Processing Letters, 2006, 13(8):493-496. doi: 10.1109/LSP.2006.873148
    [7] ZHENG J H, LIU J F.A robust finite-horizon Kalman filter for uncertain discrete time-varying systems with state-delay and missing measurements[J].International Journal of Grid and Distributed Computing, 2016, 9(3):229-242.
    [8] WANG F, WANG Z D, LIANG J L, et al.Robust finite-horzion filtering for 2-D systems with randomly varying sensor delays[J].IEEE Transactions on Systems, Man, and Cybernetics:Ssytems, 2018:1-13. http://ieeexplore.ieee.org/document/8260972/
    [9] QIN W T, WANG X G, BAI Y L, et al.Arbitrary-step randomly delayed robust filter with application to boost phase tracking[J].Acta Astronautica, 2018, 145:304-318. doi: 10.1016/j.actaastro.2018.01.056
    [10] FAN Z, YANG J.A research of gyro/star-sensor integrated attitude determination based on particle filter[C]//Third International Conference on Instrumentation, Measurement, Computer, Communication and Control.Piscataway, NJ: IEEE Press, 2013: 256-261.
    [11] REIF K, GVNTHER S, YAZ E, et al.Stochastic stability of the discrete-time extended Kalman filter[J].IEEE Transactions on Automatic Control, 1999, 44(4):714-728. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=07093f2f97280d5338d49a76c357574d
    [12] XIONG K, LIU L D, ZHANG H Y.Modified unscented Kalman filtering and its application in autonomous satellite navigation[J].Aerospace Science & Technology, 2009, 13(4):238-246. doi: 10.1016-j.ast.2009.04.001/
    [13] WANG S, FANG H, TIAN X.Recursive estimation for nonlinear stochastic systems with multi-step transmission delays, multiple packet dropouts and correlated noises[J].Signal Processing, 2015, 115:164-175. doi: 10.1016/j.sigpro.2015.03.022
    [14] 黄蔚.CKF及鲁棒滤波在飞行器姿态估计中的应用研究[D].哈尔滨: 哈尔滨工程大学, 2015.

    HUANG W.Application of CKF and robust filter in aircraft attitude estimation[D].Harbin: Harbin Engineering University, 2015(in Chinese).
    [15] XIE L, SOH Y C, DE SOUZA C E.Robust Kalman filtering for uncertain discrete-time systems[J].IEEE Transactions on Automatic Control, 1994, 39(6):1310-1314. doi: 10.1109/9.293203
  • 加载中
图(4)
计量
  • 文章访问数:  619
  • HTML全文浏览量:  56
  • PDF下载量:  261
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-05-17
  • 录用日期:  2018-08-24
  • 网络出版日期:  2019-02-20

目录

    /

    返回文章
    返回
    常见问答