NDLI: GNSS/INS sensor fusion using Kalman filter with covariance adaptation

Content Provider	IEEE Xplore Digital Library
Author	Chi-Yuan Cheng Kuan-Chun Sun Jwu-Sheng Hu
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Chi-Yuan Cheng; Kuan-Chun Sun; Jwu-Sheng Hu)
Abstract	This paper proposes a new method which estimates the Kalman filter's measurement noise covariance matrix in a loosely coupled GNSS/INS sensor fusion system. The signals received from the satellites are heavily influenced by the environmental conditions, and will cause the noise variance to change dramatically. Thus, estimating the measurement noise to improve the fusion quality is very important. Most of the proposed methods use the moving average method to estimate the measurement noise online from the innovation sequence of the Kalman filter in the window interval. However, those methods can only have a good performance in a quasi-static environment, e.g., freeway where the signals are rarely blocked. In dynamic environments such as the urban area, the satellite signals are often blocked by the buildings, resulting in a large range of the noise variance. This paper proposes to use Savitzky-Golay filter with degree adaptation (ADSG) to estimate the measurement noise. The ADSG filter uses local polynomial regression method to fit the delayed-sample innovation sequence in the window interval, and adjust the polynomial order by the statistics F-test. Experiments show that the proposed method's horizontal position error is about 0.3m, which is better than several existing methods.
Starting Page	52
Ending Page	57
File Size	1816043
Page Count	6
File Format	PDF
e-ISBN	9781479923830
DOI	10.1109/CACS.2013.6734106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Technological innovation Motion trajectory Measurement noise estimation Noise Estimation Savitzky-Golay filter Noise measurement Kalman filters Innovation sequence Covariance matrices Global Positioning System
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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