An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals

Long Zhao; Hongyu Yan

doi:10.1007/978-3-642-37407-4_67

An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals

Long Zhao^*
, Hongyu Yan

^*此作品的通讯作者

自动化科学与电气工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

In order to overcome the drawbacks of the fault detection method based on χ 2 test that is insensitive to soft fault detection, an adaptive dynamic robust Kalman based on variance inflation model was developed, which can detect the soft fault of system. The proposed method cumulates the residuals in open windows. When the cumulant surpasses the threshold, the error covariance is enlarged to prevent abnormal Global Positioning System (GPS) observations. This method has been applied to integrated navigation system of Inertial Navigation System/Global Navigation Satellite System (INS/GNSS). The simulation results show that the soft fault is detected by using adaptive dynamic robust Kalman, and the filtering precision is higher than the traditional Kalman filtering algorithm.

源语言	英语
主期刊名	China Satellite Navigation Conference, CSNC 2013 - Proceedings
主期刊副标题	Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods
出版商	Springer Verlag
页	727-735
页数	9
ISBN（印刷版）	9783642374067
DOI	https://doi.org/10.1007/978-3-642-37407-4_67
出版状态	已出版 - 2013
活动	4th China Satellite Navigation Conference, CSNC 2013 - Wuhan, 中国期限: 13 5月 2013 → 17 5月 2013

出版系列

姓名	Lecture Notes in Electrical Engineering
卷	245 LNEE
ISSN（印刷版）	1876-1100
ISSN（电子版）	1876-1119

会议

会议	4th China Satellite Navigation Conference, CSNC 2013
国家/地区	中国
市	Wuhan
时期	13/05/13 → 17/05/13

访问文件

10.1007/978-3-642-37407-4_67

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhao, L., & Yan, H. (2013). An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals. 在 China Satellite Navigation Conference, CSNC 2013 - Proceedings: Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods (页码 727-735). (Lecture Notes in Electrical Engineering; 卷 245 LNEE). Springer Verlag. https://doi.org/10.1007/978-3-642-37407-4_67

Zhao, Long ; Yan, Hongyu. / An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals. China Satellite Navigation Conference, CSNC 2013 - Proceedings: Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods. Springer Verlag, 2013. 页码 727-735 (Lecture Notes in Electrical Engineering).

@inproceedings{576d5f02e1504f3e99c0fd8c4808f2b6,

title = "An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals",

abstract = "In order to overcome the drawbacks of the fault detection method based on χ 2 test that is insensitive to soft fault detection, an adaptive dynamic robust Kalman based on variance inflation model was developed, which can detect the soft fault of system. The proposed method cumulates the residuals in open windows. When the cumulant surpasses the threshold, the error covariance is enlarged to prevent abnormal Global Positioning System (GPS) observations. This method has been applied to integrated navigation system of Inertial Navigation System/Global Navigation Satellite System (INS/GNSS). The simulation results show that the soft fault is detected by using adaptive dynamic robust Kalman, and the filtering precision is higher than the traditional Kalman filtering algorithm.",

keywords = "Fault detection, Integrated navigation, Kalman filtering, Robust filtering",

author = "Long Zhao and Hongyu Yan",

year = "2013",

doi = "10.1007/978-3-642-37407-4\_67",

language = "英语",

isbn = "9783642374067",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "727--735",

booktitle = "China Satellite Navigation Conference, CSNC 2013 - Proceedings",

address = "德国",

note = "4th China Satellite Navigation Conference, CSNC 2013 ; Conference date: 13-05-2013 Through 17-05-2013",

}

Zhao, L & Yan, H 2013, An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals. 在 China Satellite Navigation Conference, CSNC 2013 - Proceedings: Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods. Lecture Notes in Electrical Engineering, 卷 245 LNEE, Springer Verlag, 页码 727-735, 4th China Satellite Navigation Conference, CSNC 2013, Wuhan, 中国, 13/05/13. https://doi.org/10.1007/978-3-642-37407-4_67

An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals. / Zhao, Long; Yan, Hongyu.
China Satellite Navigation Conference, CSNC 2013 - Proceedings: Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods. Springer Verlag, 2013. 页码 727-735 (Lecture Notes in Electrical Engineering; 卷 245 LNEE).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals

AU - Zhao, Long

AU - Yan, Hongyu

PY - 2013

Y1 - 2013

N2 - In order to overcome the drawbacks of the fault detection method based on χ 2 test that is insensitive to soft fault detection, an adaptive dynamic robust Kalman based on variance inflation model was developed, which can detect the soft fault of system. The proposed method cumulates the residuals in open windows. When the cumulant surpasses the threshold, the error covariance is enlarged to prevent abnormal Global Positioning System (GPS) observations. This method has been applied to integrated navigation system of Inertial Navigation System/Global Navigation Satellite System (INS/GNSS). The simulation results show that the soft fault is detected by using adaptive dynamic robust Kalman, and the filtering precision is higher than the traditional Kalman filtering algorithm.

AB - In order to overcome the drawbacks of the fault detection method based on χ 2 test that is insensitive to soft fault detection, an adaptive dynamic robust Kalman based on variance inflation model was developed, which can detect the soft fault of system. The proposed method cumulates the residuals in open windows. When the cumulant surpasses the threshold, the error covariance is enlarged to prevent abnormal Global Positioning System (GPS) observations. This method has been applied to integrated navigation system of Inertial Navigation System/Global Navigation Satellite System (INS/GNSS). The simulation results show that the soft fault is detected by using adaptive dynamic robust Kalman, and the filtering precision is higher than the traditional Kalman filtering algorithm.

KW - Fault detection

KW - Integrated navigation

KW - Kalman filtering

KW - Robust filtering

UR - https://www.scopus.com/pages/publications/84880717668

U2 - 10.1007/978-3-642-37407-4_67

DO - 10.1007/978-3-642-37407-4_67

M3 - 会议稿件

AN - SCOPUS:84880717668

SN - 9783642374067

T3 - Lecture Notes in Electrical Engineering

SP - 727

EP - 735

BT - China Satellite Navigation Conference, CSNC 2013 - Proceedings

PB - Springer Verlag

T2 - 4th China Satellite Navigation Conference, CSNC 2013

Y2 - 13 May 2013 through 17 May 2013

ER -

Zhao L, Yan H. An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals. 在 China Satellite Navigation Conference, CSNC 2013 - Proceedings: Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods. Springer Verlag. 2013. 页码 727-735. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-3-642-37407-4_67

An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals

摘要

出版系列

会议

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学术指纹

引用此