IMM method using tracking filter with fuzzy gain

Sun Young Noh, Jin Bae Park, Young Hoon Joo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMICAI 2006
Subtitle of host publicationAdvances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings
Pages756-766
Number of pages11
Publication statusPublished - 2006 Dec 1
Event5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence - Apizaco, Mexico
Duration: 2006 Nov 132006 Nov 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4293 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence
CountryMexico
CityApizaco
Period06/11/1306/11/17

Fingerprint

Multiple Models
Filter
Target
Fuzzy systems
Genetic algorithms
Fuzzy Systems
Computer Simulation
Optimise
Computer simulation
Genetic Algorithm
Unknown
Model
Estimate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Noh, S. Y., Park, J. B., & Joo, Y. H. (2006). IMM method using tracking filter with fuzzy gain. In MICAI 2006: Advances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings (pp. 756-766). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4293 LNAI).
Noh, Sun Young ; Park, Jin Bae ; Joo, Young Hoon. / IMM method using tracking filter with fuzzy gain. MICAI 2006: Advances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings. 2006. pp. 756-766 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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.",
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year = "2006",
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Noh, SY, Park, JB & Joo, YH 2006, IMM method using tracking filter with fuzzy gain. in MICAI 2006: Advances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4293 LNAI, pp. 756-766, 5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence, Apizaco, Mexico, 06/11/13.

IMM method using tracking filter with fuzzy gain. / Noh, Sun Young; Park, Jin Bae; Joo, Young Hoon.

MICAI 2006: Advances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings. 2006. p. 756-766 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4293 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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Noh SY, Park JB, Joo YH. IMM method using tracking filter with fuzzy gain. In MICAI 2006: Advances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings. 2006. p. 756-766. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).