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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.8 P.615-628


SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking

Author(s):  Quan-bo Ge, Wen-bin Li, Cheng-lin Wen

Affiliation(s):  Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China, State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   qbge@hdu.edu.cn, wencl@hdu.edu.cn

Key Words:  Nonlinear system, Maneuver target tracking, Correlated noises, Square-root cubature Kalman filter (SCKF), Strong tracking filtering (STF)

Quan-bo Ge, Wen-bin Li, Cheng-lin Wen. SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking[J]. Journal of Zhejiang University Science C, 2011, 12(8): 615-628.

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%T SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking
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%A Wen-bin Li
%A Cheng-lin Wen
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C10a0353

T1 - SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking
A1 - Quan-bo Ge
A1 - Wen-bin Li
A1 - Cheng-lin Wen
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 8
SP - 615
EP - 628
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C10a0353

square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Arasaratnam, I., Haykin, S., 2008. Square-root quadrature Kalman filtering. IEEE Trans. Signal Process., 56(6):2589-2593.

[2]Arasaratnam, I., Haykin, S., 2009. Cubature Kalman filters. IEEE Trans. Autom. Control, 54(6):1254-1269.

[3]Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T., 2002. A tutorial on particle filters for online nonlinear/ non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 50(2):174-188.

[4]Daum, F., 2005. Nonlinear filters: beyond the Kalman filter. IEEE Aerosp. Electron. Syst. Mag., 20(8):57-69.

[5]Deng, X.L., Xie, J.Y., Guo, W.Z., 2006. Adaptive particle filtration for state estimation. J. South China Univ. Technol. (Nat. Sci. Ed.), 34(1):57-61 (in Chinese).

[6]He, Y., Song, Q., Dong, Y.L., Yang, J., 2006. Adaptive Tracking Algorithm Based on Modified Strong Tracking Filter. Int. Conf. on Radar, p.1-4.

[7]Julier, S.J., Uhlmann, J.K., 1997. A new extension of the Kalman filter to nonlinear systems. SPIE, 3068:182-193.

[8]Julier, S.J., Uhlmann, J.K., 2004. Unscented filtering and nonlinear estimation. Proc. IEEE, 92(3):401-422.

[9]Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng., 82(1):35-45.

[10]Li, W.B., Ge, Q.B., 2010. UKF-STF Tracking with Correlated Noises for the Nonlinear System. 8th World Congress on Intelligent Control and Automation, p.3466-3471.

[11]Pan, Q., Yang, F., Liang, Y., 2005. Survey of a kind of nonlinear filters. Control Dec. China, 20(5):481-494 (in Chinese).

[12]Wan, E.A., van der Merwe, R., 2000. The Unscented Kalman Filter for Nonlinear Estimation. IEEE Adaptive Systems for Signal Processing, Communications, and Control Symp., p.153-158.

[13]Wu, Y.X., Hu, D.W., 2005. Unscented Kalman filtering for additive noise case: augmented vs. non-augmented. IEEE Signal Process. Lett., 12(5):357-360.

[14]Xiong, K., Zhang, H.Y., Chan, C.W., 2006. Performance evaluation of UKF-based nonlinear filtering. Automatica, 42(2):261-270.

[15]Xu, W.M., Kuang, L.L., Lu, J.H., 2009. Positioning algorithm with joint space-time constraints for unmanned network-flying vehicles. IEEE Trans. Geosci. Remote Sens., 47(8):2694-2704.

[16]Zhou, D.H., Ye, Y.Z., 2000. Modern Fault Diagnosis and Fault-Tolerant Control. Tsinghua University Press, Beijing, China, p.60-116 (in Chinese).

[17]Zhou, D.H., Xi, Y.G., Zhang, Z.J., 1999. A suboptimal multiple fading extended Kalman filter. Acta Autom. Sin. China, 17(6):689-695.

[18]Zhu, T.J., Zheng, H.G., 2008. Application of Unscented Kalman Filter to Vehicle State Estimation. ISECS Int. Colloquium on Computing, Communication, Control, and Management, p.135-139.

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