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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Du, Z., Cai, Z., Chen, L., Deng, H. (2009). An Evolutionary Algorithm and Kalman Filter Hybrid Approach for Integrated Navigation. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_23
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DOI: https://doi.org/10.1007/978-3-642-04843-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
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