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Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques
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 Title & Authors
Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques
Kim, Chulpyo; Noh, Sanguk; Park, So Ryoung;
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In this paper, we propose a method to analyze the clusters of RF threats emitting electrical signals based on collected signal variables in integrated electronic warfare environments. We first analyze the signal variables collected by an electronic warfare receiver, and construct a model based on variables showing the properties of threats. To visualize the distribution of RF threats and reversely identify them, we use k-means clustering algorithm and self-organizing map (SOM) algorithm, which are belonging to unsupervised learning techniques. Through the resulting model compiled by k-means clustering and SOM algorithms, the RF threats can be classified into one of the distribution of RF threats. In an experiment, we measure the accuracy of classification results using the algorithms, and verify the resulting model that could be used to visually recognize the distribution of RF threats.
RF Threats;Unsupervised Learning;Self-Organizing Map;K-Means Clustering Algorithm;Integrated Electronic Warfare;
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점진적 기계학습 기반의 레이다 위협체 역추정 시뮬레이터 설계,노상욱;

한국전자파학회지:전자파기술, 2016. vol.27. 6, pp.13-19
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