Abstract
In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking error for maneuvering target. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After an acceleration input is detected, the state estimate for each sub-model is modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). Finally, the tracking performance of the proposed method is compared with those of the input estimation(IE) method and AIMM method through computer simulations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Grewal, M.S., Andrews, A.P.: Kalman filtering: theory and practice. Prentice-Hall, Englewood Cliffs (1993)
Singer, R.A.: Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Trans. Aeros. Electro. Syst. 6, 473–483 (1969)
Chan, Y.T., Hu, A.G.C., Plant, J.B.: A Kalman filter based tracking scheme with input estimation. IEEE Trans. Aeros. Electro. Syst. 15, 237–244 (1979)
Guu, J.A., Wei, C.H.: Tracking a maneuvering target using input estimation at high measurement frequency. International Journal of System Science 23, 871–883 (1992)
Bar-Shalom, Y., Birmiwal, K.: Variable dimension filter for maneuvering target tracking. IEEE Trans. Aeros. Electro. Syst. 18, 621–629 (1982)
Alouani, A.T., Price, P., Blair, W.D.: A two-stage Kalman estimator for state estimation in the presence of random bias for tracking maneuvering targets. In: Proc. Of 30th IEEE Conf. Decision and Control, pp. 2002–2059 (1991)
Tugnait, J.K.: Detection and estimation for abruptly changing systems. IEEE Trans. Autom. 18, 607–615 (1982)
Bar-Shalom, Y., Li, X.: Principles, Techniques and Software. Arteck House, Norwood (1993)
Munir, A., Atherton, D.P.: Adaptive interacting multiple model algorithm for tracking a maneuvering target. IEE Proc. of Radar. Sonar Navig. 142, 11–17 (1995)
Li, T.H.S.: Estimation of one-dimensional radar tracking via fuzzy-Kalman filter. In: Proceedings of the IECON 1993 International Conference, pp. 2384–2388 (1993)
Joo, Y.H., Hwang, Kim, K.B., Woo, K.B.: Fuzzy system modeling by fuzzy partition and GA hybrid schemes. Fuzzy Sets and Systems 86, 279–288 (1997)
Wang, L.X.: A course in fuzzy systems and control. Prentice-Hall, Englewood Cliffs (1998)
Carse, B., Terence, C., Munro, A.: Evolving fuzzy rule based controllers using genetic algorithm. Fuzzy Sets and Systems 80, 273–293 (1996)
McGinnity, S., Irwin, G.W.: Fuzzy logic approach to maneuvering target tracking. IEE proc. Of Radar Sonar and Navigation. 145, 337–341 (1998)
Munir, A., Artherton, P.: Adaptive interacting multiple model algorithm for tracking a maneuvering target. IEE Proc. Radar Sonar Navig. 142, 11–17 (1995)
Lee, B.J., Park, J.B., Joo, H.Y., Jin, S.H.: An Intelligent Tracking Method for Maneuvering Target. International Journal of Contr. Automation and Systems 1, 93–100 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Noh, S.Y., Park, J.B., Joo, Y.H. (2006). IMM Method Using Tracking Filter with Fuzzy Gain. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_72
Download citation
DOI: https://doi.org/10.1007/11925231_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
eBook Packages: Computer ScienceComputer Science (R0)