A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 4 Issue 1
Jan.  2017

IEEE/CAA Journal of Automatica Sinica

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Wei Sun and Yongjian Yang, "Adaptive Maneuvering Frequency Method of Current Statistical Model," IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 154-160, Jan. 2017.
Citation: Wei Sun and Yongjian Yang, "Adaptive Maneuvering Frequency Method of Current Statistical Model," IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 154-160, Jan. 2017.

Adaptive Maneuvering Frequency Method of Current Statistical Model


Natural Science Foundation Research Project of Shanxi Science and Technology Department 2016JM1032

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  • Current statistical model (CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter (KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules, so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.


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