$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

IMM Method Using Kalman Filter with Fuzzy Gain 원문보기

퍼지 및 지능시스템학회 논문지 = Journal of fuzzy logic and intelligent systems, v.16 no.2, 2006년, pp.234 - 239  

노선영 (Department of Electrical and Electronic Engineering, Yonsei University) ,  주영훈 (School of Electronic and Information Engineering, Kunsan National University) ,  박진배 (Department of Electrical and Electronic Engineering, Yonsei University)

Abstract ▼ AI-Helper 아이콘AI-Helper

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target ...

주제어

#Interacting multiple model   #Maneuvering target tracking   #Intelligent tracking filter   #Fuzzy system   #Genetic algorithm  

AI 본문요약
AI-Helper 아이콘 AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

  • Then, multiple models are represented as the acceleration levels estimated by these fuzzy systems, which are optimized for different ranges of acceleration input. Finally, the tracking performance of the proposed method is compared with those of the input estimation(IE) algorithm method and the IMM algorithm method through computer simulations.
  • with fuzzy gain. In the proposed method, the unknown acceleration level for each sub-model was determined by proposed method which is the estimation of the acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. And then, modified sub-model are corrected by the new update equation method which is a fuzzy system using the relation between the filter residual and its variation.
  • In this paper, we have developed IMM tracking algorithm with fuzzy gain. In the proposed method, the unknown acceleration level for each sub-model was determined by proposed method which is the estimation of the acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one.
  • First, when the target maneuver occurs, the acceleration level for each sub-model is determined by the using the fuzzy system based on the illation between the non-maneuvering filter residual and the maneuvering one at every sampling time. Second, to modify the accurate estimation, the target with maneuver is updated by using the fuzzy gain based on the fuzzy model. Since it is hard to approximate adaptively this time-varying variance and fuzzy gain owing to the highly nonlinear, a fuzzy system is appHed as the universal approximator to compute it.
  • The algorithm improves the tracking performance and establishes the systematic tracker design procedure for a maneuvering target. The complete solution can be divided into two stages.
  • To evaluate the proposed filtering scheme, a maneuvering target scenario was examined and the theoretical analysis from the previous section show how to determined and updated for the maneuvering target model. For comparison purposes, we also simulated conventional the input estimation method (IE) and the adaptive interacting multiple method (AIMM) methods.

이론/모형

  • The interesting one of them is the interacting multiple mod이 (IMM) approach[6]. In the algorithm, a parallel bank of filters are blended in a weighted-sum form by an underlying finite-dimensional Markov chain so that a smooth trans辻ion between sub-models is achieved. However, to realize a target tracker with an outstanding performance, a prior statistical knowledge on the maneuvering target should be supplied, ie, the process noise variance for each sub-model in IMM should be accurately selected in advance by the domain expert who should fully understand the unknown maneuvering characteristics of the target, which is not an easy ta아匚
본문요약 정보가 도움이 되었나요?

참고문헌 (13)

  1. R. A. Singer, 'Estimating optimal tracking filter performance for manned maneuvering targets,' IEEE Trans. Aerosp. Electron Syst., Vol AES-6, No.4, pp. 473-483, July, 1970 

    상세보기 crossref

  2. J. Korn, S. W. Gully, and A. S. Willsky, 'Application of the generalized likelihood ratio algorithm to maneuver detection and estimation,' American Control Conference, pp. 792-798, Jun 1982 

  3. Y. T. Chan, A. G. C. Hu, and J.B. Plant, 'A Kalman filter based tracking scheme with input estimation,' IEEE Trans. on Aerospace and Electronic Systems, Vol. AES-15, No.2, pp. 237-244, Mar, 1979 

    상세보기 crossref

  4. P. L. Bogler, 'Tracking a maneuvering target using input estimation,' IEEE Trans. on Aerospace and Electronic Systems, Vol. AES-23, No.3, pp. 298-310, 1987 

    상세보기 crossref

  5. M. S. Grewal and A. P. Andrews, Kalman filtering: theory and practice, Prentice Hall, 1993 

  6. Bar-Shalom Y. and Li X.: 'Principles, Techniques and Software', Norwood, MA: Arteck House, 1993 

  7. Y. Bar-Shalom and X. Li, Estimation and Tracking: Principles, Techniques and Software, Artech House, 1993 

  8. B. J. Lee, Y. H. Joo, and J. B. Park, 'The design of target tracking system using the identification of TS fuzzy model,' Proc. of KIEE Summer Annual Conference 2001, pp. 1958-1960, 2001 

  9. B. J. Lee, Y. H. Joo, and J. B. Park, 'An intelligent tracking method for a maneuvering target,' International Journal of control, Automation and Systems, Vol. 1, No.1, March, 2003 

    원문보기 상세보기

  10. D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison -wesley, 1989 

  11. S. McGinnity and G. W. Irwin, 'Fuzzy logic approach to maneuvering target tracking,' IEE proc. of Radar, Sonar, and Navigation, Vol. 145, No.6, pp. 337-341, 1998 

    상세보기 crossref

  12. B. J. Lee, J. B, Park, H. J. Lee and Y. H. Joo, 'Fuzzy-logic-based IMM algorithm for tracking a manoeuvring target,' IEE Proc. Radar Sonar Navig., Vol. 152, No. 1, pp. 16-22, 2005 

    상세보기 crossref

  13. Y. Bar-Shalom and K. Birmiwal, 'Variable dimension filter for maneuvering target tracking,' IEEE Trans. on Aerospace and Electronic Systems, Vol. AES-18, No.5, pp. 621-629, 1982 

    상세보기 crossref

저자의 다른 논문 :

관련 콘텐츠

오픈액세스(OA) 유형

BRONZE

출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로