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Open AccessArticle

Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

by Hua Liu and Wen Wu *
Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(4), 741; https://doi.org/10.3390/s17040741
Received: 20 February 2017 / Revised: 26 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
Conventional spherical simplex-radial cubature Kalman filter (SSRCKF) for maneuvering target tracking may decline in accuracy and even diverge when a target makes abrupt state changes. To overcome this problem, a novel algorithm named strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. The proposed algorithm uses the spherical simplex-radial (SSR) rule to obtain a higher accuracy than cubature Kalman filter (CKF) algorithm. Meanwhile, by introducing strong tracking filter (STF) into SSRCKF and modifying the predicted states’ error covariance with a time-varying fading factor, the gain matrix is adjusted on line so that the robustness of the filter and the capability of dealing with uncertainty factors is improved. In this way, the proposed algorithm has the advantages of both STF’s strong robustness and SSRCKF’s high accuracy. Finally, a maneuvering target tracking problem with abrupt state changes is used to test the performance of the proposed filter. Simulation results show that the STSSRCKF algorithm can get better estimation accuracy and greater robustness for maneuvering target tracking. View Full-Text
Keywords: maneuvering target tracking; spherical simplex-radial rule; cubature Kalman filter; fading factor; strong tracking filter maneuvering target tracking; spherical simplex-radial rule; cubature Kalman filter; fading factor; strong tracking filter
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Liu, H.; Wu, W. Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking. Sensors 2017, 17, 741.

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