An Evolutionary Algorithm and Kalman Filter Hybrid Approach for Integrated Navigation

Authors:
Zhiqiang Du;Zhihua Cai;Leichen Chen;Huihui Deng
Affiliations:
School of Computer, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

Kalman filter perform optimally when the noise statistics for the measurement and process are completely known in integrated navigation system. However, the noise statistics could change with the actual working environment and so the initial priori value would represent the actual state of noise incorrectly. To solve this problem, this paper presents an adaptive Kalman Filter based on evolutionary algorithm. The hybrid method improves the real-time noise statistics by the procedure of global search. Field test data are processed to evaluate the performance of the proposed method. The results of experiment show the proposed method is capable of improving the output precision and adaptive capacity of filtering, and thus is valuable in application.