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Proceedings Paper

Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments
Author(s): Qiyao Zuo; Hong Yuan; Baojun Lin
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Paper Abstract

GPS C/A signal tracking algorithms have been developed based on adaptive Kalman filtering theory. In the research, an adaptive Kalman filter is used to substitute for standard tracking loop filters. The goal is to improve estimation accuracy and tracking stabilization in high noise and high dynamic environments. The linear dynamics model and the measurements model are designed to estimate code phase, carrier phase, Doppler shift, and rate of change of Doppler shift. Two adaptive algorithms are applied to improve robustness and adaptive faculty of the tracking, one is Sage adaptive filtering approach and the other is strong tracking method. Both the new algorithms and the conventional tracking loop have been tested by using simulation data. In the simulation experiment, the highest jerk of the receiver is set to 10G m/s3 with the lowest C/No 30dBHz. The results indicate that the Kalman filtering algorithms are more robust than the standard tracking loop, and performance of tracking loop using the algorithms is satisfactory in such extremely adverse circumstances.

Paper Details

Date Published: 10 November 2007
PDF: 6 pages
Proc. SPIE 6795, Second International Conference on Space Information Technology, 67957T (10 November 2007); doi: 10.1117/12.775470
Show Author Affiliations
Qiyao Zuo, Ctr. for Space Science and Applied Research (China)
Academy of Optoelectronics (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Hong Yuan, Academy of Optoelectronics (China)
Baojun Lin, Academy of Optoelectronics (China)

Published in SPIE Proceedings Vol. 6795:
Second International Conference on Space Information Technology
Cheng Wang; Shan Zhong; Jiaolong Wei, Editor(s)

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