基于新息χ2检测的扩展抗差卡尔曼滤波及其应用

苗岳旺, 周巍, 田亮, 崔志伟

苗岳旺, 周巍, 田亮, 崔志伟. 基于新息χ2检测的扩展抗差卡尔曼滤波及其应用[J]. 武汉大学学报 ( 信息科学版), 2016, 41(2): 269-273. DOI: 10.13203/j.whugis20130666
引用本文: 苗岳旺, 周巍, 田亮, 崔志伟. 基于新息χ2检测的扩展抗差卡尔曼滤波及其应用[J]. 武汉大学学报 ( 信息科学版), 2016, 41(2): 269-273. DOI: 10.13203/j.whugis20130666
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MIAO Yuewang, ZHOU Wei, TIAN Liang, CUI Zhiwei. Extended Robust Kalman Filter Based on Innovation Chi-Square Test Algorithm and Its Application[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 269-273. DOI: 10.13203/j.whugis20130666
Citation: MIAO Yuewang, ZHOU Wei, TIAN Liang, CUI Zhiwei. Extended Robust Kalman Filter Based on Innovation Chi-Square Test Algorithm and Its Application[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 269-273. DOI: 10.13203/j.whugis20130666
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基于新息χ2检测的扩展抗差卡尔曼滤波及其应用

  • 1.

    西安测绘总站, 陕西西安, 710054;

  • 2.

    61206部队, 北京, 100042

基金项目: 国家自然科学基金(41474015);大地测量与地球动力学重点实验室开放基金(SKLGED2014-3-6-E);地理信息工程国家重点实验室开放研究基金(SKLGIE2015-M-1-2)。
详细信息
    作者简介:

    苗岳旺,硕士,主要从事大地测量数据处理研究。my2012m@163.com

  • 中图分类号: P228;P207

Extended Robust Kalman Filter Based on Innovation Chi-Square Test Algorithm and Its Application

  • 1.

    Xi'an Division of Surveying and Mapping, Xi'an 710054, China;

  • 2.

    Unit 61206, Beijing 100042, China

Funds: The National Natural Science Foundation of China, No. 41474015; the Opening Fund of State Key Laboratory of Geodesy and Earth’s Dynamics, No. SKLGED2014-3-6-E; the Open Research Fund Program of State Key Laboratory of Geo-information Engineering, No. SKLGIE2015-M-1-2.

摘要

    摘要
  • 摘要: 针对INS/GNSS松组合导航中GNSS因信号受到遮挡和干扰而产生的位置、速度观测故障问题,研究了新息χ2检测算法,在此基础上提出了一种扩展抗差卡尔曼滤波,对容易受到遮挡和干扰的GNSS位置、速度观测量进行了抗差处理,解决了在缺少多余观测量时难以进行抗差滤波的问题,最后用实测数据对算法进行了验证。实验结果表明,基于新息χ2检测的扩展抗差卡尔曼滤波在没有多余观测量的情况下也能够有效抑制观测粗差的影响,提高组合导航系统的稳定性和可靠性。
    Abstract: An extended robust Kalman filter based on the chi-square test algorithm was developed to address the observation failure for position and velocity due to the fact that GNSS signals are sheltered as they interfere with integrated navigation.The algorithm was used for anti-processing the GNSS position and velocity observations allowing the use of the robust Kalman filter in situations lacking redundant observations. Finally, measured data was processed to verify the algorithm. Results show that observation outliers can be controlled effectively while the filter stability and reliability is improved by the extended robust Kalman filter based on chi-square test algorithm, even if there are no redundant observations.
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    • 参考文献(11)
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出版历程
  • 收稿日期:  2014-03-05
  • 发布日期:  2016-02-04

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