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CLC number: TN911.72
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 14
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Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler
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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.
@article{title="Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler",
author="DU Shi-chuan, SHI Zhi-guo, ZANG Wei, CHEN Kang-sheng",
journal="Journal of Zhejiang University Science A",
volume="8",
number="8",
pages="1277-1282",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1277"
}
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%A ZANG Wei
%A CHEN Kang-sheng
%J Journal of Zhejiang University SCIENCE A
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%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%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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A1 - CHEN Kang-sheng
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 8
SP - 1277
EP - 1282
%@ 1673-565X
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.
[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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