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

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	China Satellite Navigation Conference, CSNC 2013 - Proceedings
Subtitle of host publication	Precise Orbit Determination and Positioning - Atomic Clock Technique and Time-Frequency System - Integrated Navigation and New Methods
Publisher	Springer Verlag
Pages	727-735
Number of pages	9
ISBN (Print)	9783642374067
DOIs	https://doi.org/10.1007/978-3-642-37407-4_67
State	Published - 2013
Event	4th China Satellite Navigation Conference, CSNC 2013 - Wuhan, China Duration: 13 May 2013 → 17 May 2013

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	245 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	4th China Satellite Navigation Conference, CSNC 2013
Country/Territory	China
City	Wuhan
Period	13/05/13 → 17/05/13

Keywords

Fault detection
Integrated navigation
Kalman filtering
Robust filtering

Access to Document

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

Cite this

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

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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",

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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. in 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, vol. 245 LNEE, Springer Verlag, pp. 727-735, 4th China Satellite Navigation Conference, CSNC 2013, Wuhan, China, 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. p. 727-735 (Lecture Notes in Electrical Engineering; Vol. 245 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yan, Hongyu

PY - 2013

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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

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SN - 9783642374067

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BT - China Satellite Navigation Conference, CSNC 2013 - Proceedings

PB - Springer Verlag

T2 - 4th China Satellite Navigation Conference, CSNC 2013

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Zhao L, Yan H. An adaptive dynamic kalman filtering algorithm based on cumulative sums of residuals. In 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. p. 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

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