Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments

Qiyao Zuo; Hong Yuan; Baojun Lin

doi:10.1117/12.775470

10 November 2007 Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments

Qiyao Zuo, Hong Yuan, Baojun Lin

Proceedings Volume 6795, Second International Conference on Space Information Technology; 67957T (2007) https://doi.org/10.1117/12.775470
Event: Second International Conference on Spatial Information Technology, 2007, Wuhan, China

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.

Citation Download Citation

Qiyao Zuo, Hong Yuan, and Baojun Lin "Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments", Proc. SPIE 6795, Second International Conference on Space Information Technology, 67957T (10 November 2007); https://doi.org/10.1117/12.775470

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