Abstract
To improve the ability of dealing with inaccurate of model and statistic characteristics of noise, as well as the abrupt change of state of cubature Kalman filter (CKF), a new nonlinear filter, called Cubature Kalman Filter based on strong tracking (CKF-ST), is proposed in this paper. Inspired by the idea of strong tracking, a time-variant factor is introduced into the recursive process of cubature Kalman filter such that the filter gain can be updated along with the measured values, thus endowing CKF-ST powerful ability to deal with abrupt changes of state. Meanwhile, such merits of CKF as high accuracy and being easy to implement can be entirely preserved in CKF-ST. Simulation results on one classical examples demonstrate that CKF-ST is overall superior to CKF and other filters involved, especially when target motion changes suddenly.
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Acknowledgments
This research was supported by Key Program of National Natural Science Foundation of China (61139003), the Fundamental Research Funds for the Central Universities (ZYGX2010J022), National Natural Science Foundation of China (No. 61139003), and the China Postdoctoral Science Foundation (No. 2013M531948).
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Cun, Z., Meng, Z., Xue-Lian, Y., Ming-Lei, C., Yun, Z., Xue-Gang, W. (2015). Cubature Kalman Filter Based on Strong Tracking. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-08991-1_14
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DOI: https://doi.org/10.1007/978-3-319-08991-1_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08990-4
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