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DOI QR Code

External Noise Analysis Algorithm based on FCM Clustering for Nonlinear Maneuvering Target

FCM 클러스터링 기반 비선형 기동표적의 외란분석 알고리즘

  • 손현승 (연세대학교 전기전자공학과) ;
  • 박진배 (연세대학교 전기전자공학과) ;
  • 주영훈 (국립 군산대학교 제어로봇 공학과)
  • Received : 2011.09.23
  • Accepted : 2011.11.16
  • Published : 2011.12.01

Abstract

This paper presents the intelligent external noise analysis method for nonlinear maneuvering target. After recognizing maneuvering pattern of the target by the proposed method, we track the state of the target. The external noise can be divided into mere noise and acceleration using only the measurement. divided noise passes through the filtering step and acceleration is punched into dynamic model to compensate expected states. The acceleration is the most deterministic factor to the maneuvering. By dividing, approximating, and compensating the acceleration, we can reduce the tracking error effectively. We use the fuzzy c-means (FCM) clustering as the method to divide external noise. FCM can separate the acceleration from the noise without criteria. It makes the criteria with the data made by measurement at every sampling time. So it can show the adaptive tracking result. The proposed method proceeds the tracking target simultaneously with the learning process. Thus it can apply to the online system. The proposed method shows the remarkable tracking result on the linear and nonlinear maneuvering. Finally, some examples are provided to show the feasibility of the proposed algorithm.

Keywords

Acknowledgement

Supported by : 한국에너지 기술평가원(KETEP)

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