Polynomial Model Set and its Interacting Multiple Model Algorithms

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Abstract:

From the maneuvering target orbit on the geometrical properties, according to different motion modes track corresponding to different order number polynomial curve, using the least squares fitting structure, this paper gives out a group of various motion modes matching the mathematical model—polynomial model set (PMS), and gives distinct mathematical process. PMS covers all the motion modes theoretically, easy to choose according to the practical situation and expand, especially suitable for single model can not accurately describe the complex sports scene. The model need not consider sampling interval, without lowering the filter performance at the same time, reduced prior knowledge dependence. Finally, simulation results indicate that the correctness and validity and practicality.

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Periodical:

Advanced Materials Research (Volumes 562-564)

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2038-2044

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August 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y. Zhang, X. R. Li: Detection and diagnosis of sensor and actuator failures using IMM estimator. IEEE Transactions on Aerospace and Electronic Systems. Vol. 34(4) (1998) pp.1293-1313.

DOI: 10.1109/7.722715

Google Scholar

[2] X. R. Li, Y. B. Shalom. Design of an interacting multiple model algorithm for air traffic control tracking. IEEE Transactions on Control Systems Technology. Vol. 3(1) (1993) pp.186-194.

DOI: 10.1109/87.251886

Google Scholar

[3] B. S. Yaakov, X. R. Li, K. Thiagalingam: Estimation with applications to tracking and navigation. New York: Awiley Interscience Publication, (2001) pp.441-466.

Google Scholar

[4] C. E. Seah, I. Hwang: Algorithm for Performance Analysis of the IMM Algorithm. IEEE Transactions on Aerospace and Electronic Systems. Vol. 47(2) (2011) pp.1114-1124.

DOI: 10.1109/taes.2011.5751246

Google Scholar

[5] X. R. Li, Y. B. Shalom: Multiple Model Estimation with Variable Structure. IEEE Trans. AC, Vol. 41(4) (1996) pp.478-493.

Google Scholar

[6] X. R. Li, P. Vesselin: Survey of Maneuvering Target Tracking Part I: Dynamic Models. IEEE Transactions on aerospace and electronic systems. Vol. 39(4) (2003) pp.1333-1364.

DOI: 10.1109/taes.2003.1261132

Google Scholar

[7] Weisstein, Eric W²Weierstrass Approximation Theorem². From Math World-A Wolfram Web Resource. http: /mathworld. wolfram. com/WeierstrassApproximationTheorem. html.

Google Scholar

[8] S. Z. Shi, S. Liu: Method and Theory of Target Tracking and Data Fusion. National Defense Industry Press, Beijing, (2010) pp.58-61. in Chinese.

Google Scholar

[9] Weisstein Eric W²Least Squares Fitting². From Math World-A Wolfram Web Resource. http: /mathworld. wolfram. com/LeastSquaresFitting. html.

Google Scholar

[10] P. Heinonen, Y. Neuvo: FIR-median hybrid filters with predictive FIR substructures. IEEE Transactions on Acoustics, Speech, and Signal processing, Vol. 36(6) (1988) pp.892-899.

DOI: 10.1109/29.1600

Google Scholar

[11] G. Welch, G. Bishop: An Introduction to the Kalman Filter. UNC-Chapel Hill, TR95-041, (2004).

Google Scholar

[12] X. L. Deng: Particle Filter Based on Interacting Multiple Model. Journal of System Simulation. Vol. 17(10) (2005) pp.2360-2362. in Chinese.

Google Scholar

[13] Y. G. Jia, Y. Liang: Interacting multiple model algorithm in transient process. Journal of System Simulation. Vol. 14(1) (2002) pp.16-18. in Chinese.

Google Scholar