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논문 기본 정보

자료유형
학술저널
저자정보
Il-Suek Koh (Inha University) Hyun Kim (LIG Nex1) Sang-Hyun Chun (LIG Nex1) Min-Kil Chong (LIG Nex1)
저널정보
한국전자파학회JEES Journal of Electromagnetic Engineering And Science Journal of Electromagnetic Engineering And Science Vol.22 No.1
발행연도
2022.1
수록면
48 - 55 (8page)

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초록· 키워드

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The classification of radar targets and clutter has been the subject of much research. Recently, artificial intelligence technology has been favored; its accuracy has been drastically improved by the incorporation of neural networks and deep learning techniques. In this paper, we consider a recurrent neural network that classifies targets and clutter sequentially measured by a weapon location radar. A raw dataset measured by a Kalman filter and an extended Kalman filter was used to train the network. The dataset elements are time, position, radial velocity, and radar cross section. To reduce the dimension of the input features, a data conversion scheme is proposed. A total of four input features were used to train the classifier and its accuracy was analyzed. To improve the accuracy of the trained network, a combined classifier is proposed, and its properties are examined. The feasibility of using the individual and combined classifiers as a real-time clutter filter is investigated.

목차

Abstract
I. INTRODUCTION
II. DATA PREPARATION
III. TRAINING NETWORK AND PERFORMANCE ASSESSMENT
IV. CONCLUSION
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