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

MIAO Yuewang; ZHOU Wei; TIAN Liang; CUI Zhiwei

doi:10.13203/j.whugis20130666

Volume 41 Issue 2

Feb. 2016

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Geomatics and Information Science of Wuhan University > 2016 > 41(2): 269-273. > DOI: 10.13203/j.whugis20130666

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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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.

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Received Date: March 05, 2014
Published Date: February 04, 2016

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Abstract

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.
- measurement innovation,
- extended robust Kalman filter,
- integrated navigation,
- loosely coupled

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