External noise analysis algorithm based on FCM clustering for nonlinear maneuvering target

Hyun Seung Son, Jin Bae Park, Young Hoon Joo

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)2346-2351
Number of pages6
JournalTransactions of the Korean Institute of Electrical Engineers
Volume60
Issue number12
DOIs
Publication statusPublished - 2011 Jan 1

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Online systems
Target tracking
Dynamic models
Sampling

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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External noise analysis algorithm based on FCM clustering for nonlinear maneuvering target. / Son, Hyun Seung; Park, Jin Bae; Joo, Young Hoon.

In: Transactions of the Korean Institute of Electrical Engineers, Vol. 60, No. 12, 01.01.2011, p. 2346-2351.

Research output: Contribution to journalArticle

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