TY - JOUR
T1 - The reduction methodology of external noise with segmentalized PSO-FCM
T2 - Its application to phased conversion of the radar system on board
AU - Son, Hyun Seung
AU - Park, Jin Bae
AU - Joo, Young Hoon
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - This paper presents an intelligent reduction method for external noise. The main idea comes from PSO-FCM (Particle Swam Optimization Fused fuzzy C-Means) clustering. The data of the target is transformed from the antenna coordinates to the vessel one and to the system coordinates. In the conversion, the overall noises hinder observer to get the exact position and velocity of the maneuvering target. While the filter is used for tracking system, unexpected acceleration becomes the main factor which makes the uncertainty. In this paper, the tracking efficiency is improved with the PSO-FCM and the compensation methodology. The acceleration is approximated from the external noise splitted by the proposed clustering method. After extracting the approximated acceleration, the rest in the noise is filtered by the filter and the compensation is added to after that. Proposed tracking method is applicable to the linear model and nonlinear one together. Also, it can do to the on-line system. Finally, some examples are provided to examine the reliability of the proposed method.
AB - This paper presents an intelligent reduction method for external noise. The main idea comes from PSO-FCM (Particle Swam Optimization Fused fuzzy C-Means) clustering. The data of the target is transformed from the antenna coordinates to the vessel one and to the system coordinates. In the conversion, the overall noises hinder observer to get the exact position and velocity of the maneuvering target. While the filter is used for tracking system, unexpected acceleration becomes the main factor which makes the uncertainty. In this paper, the tracking efficiency is improved with the PSO-FCM and the compensation methodology. The acceleration is approximated from the external noise splitted by the proposed clustering method. After extracting the approximated acceleration, the rest in the noise is filtered by the filter and the compensation is added to after that. Proposed tracking method is applicable to the linear model and nonlinear one together. Also, it can do to the on-line system. Finally, some examples are provided to examine the reliability of the proposed method.
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U2 - 10.5302/J.ICROS.2012.18.7.638
DO - 10.5302/J.ICROS.2012.18.7.638
M3 - Article
AN - SCOPUS:84867908786
VL - 18
SP - 638
EP - 643
JO - Journal of Institute of Control, Robotics and Systems
JF - Journal of Institute of Control, Robotics and Systems
SN - 1976-5622
IS - 7
ER -