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CLC number: TN911.72

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Received: 2007-03-01

Revision Accepted: 2007-06-18

Crosschecked: 0000-00-00

Cited: 14

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.8 P.1277~1282

http://doi.org/10.1631/jzus.2007.A1277


Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler


Author(s):  DU Shi-chuan, SHI Zhi-guo, ZANG Wei, CHEN Kang-sheng

Affiliation(s):  Department of Information and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   shizg@zju.edu.cn

Key Words:  Interacting multiple model, Particle filter, Blind Doppler


DU Shi-chuan, SHI Zhi-guo, ZANG Wei, CHEN Kang-sheng. Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler[J]. Journal of Zhejiang University Science A, 2007, 8(8): 1277~1282.

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author="DU Shi-chuan, SHI Zhi-guo, ZANG Wei, CHEN Kang-sheng",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1277"
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%A SHI Zhi-guo
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%J Journal of Zhejiang University SCIENCE A
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%D 2007
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%DOI 10.1631/jzus.2007.A1277

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T1 - Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler
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J0 - Journal of Zhejiang University Science A
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EP - 1282
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Y1 - 2007
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A1277


Abstract: 
In airborne tracking, the blind Doppler makes the target undetectable, resulting in tracking difficulties. In this paper, we studied most possible blind-Doppler cases and summed them up into two types: targets’ intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model (IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.

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

Reference

[1] Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T., 2002. A tutorial on particle filters for online nonlinear/ non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 50(2):174-187.

[2] Blom, A.P., Bar-Shalom, Y., 1988. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. on Automatic Control, 33(8):780-783.

[3] Boers, Y., Driessen, J.N., 2003. Interacting multiple model particle filter. IEE Proc. RSN, 150(5):344-349.

[4] Gordon, N.J., Ristic, B., 2002. Tracking airborne targets occasionally hidden in the blind Doppler. Digital Signal Processing, 12:383-393.

[5] Li, X.R., Jilkov, V.P., 2005. Survey of maneuvering target tracking. Part V: Multiple-model methods. IEEE Trans. on Aero. Electr. Syst., 41(4):1255-1321.

[6] Mo, L.B., Song, X.Q., Zhou, Y.Y., Sun, Z.K., Yaakov, B., 1998. Unbiased converted measurements for tracking. IEEE Trans. on Aero. Electr. Syst., 34(3):1023-1027.

[7] Ristic, B., Arulampalam, S., Gordon, N., 2004. Beyond the Kalman Filter: Particle Filter for Tracking Applications. Artech House.

[8] Zang, W., Shi, Z.G., Du, S.C., Chen, K.S., 2007. Novel roughening method for reentry vehicle tracking using particle filter. J. Electromagn. Waves & Appl., 21(14):1969-1981.

[9] Zaugg, D.A., Samuel, A.A., Waagen, D.E., Schmitt, H.A., 2003. A Combined Particle/Kalman Filter for Improved Tracking of Beam Aspect Targets. IEEE Workshop on Statistical Signal Processing, p.535-538.

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