Radio Anomaly Signal Recognition Methods Based on Clustering. Issue 4 (April 2020)

• Record Type: Journal Article
• Authors: Li, Shengyang; Lin, Jianxi; Tian, Bin; Shen, Li; Cui, Guorong; He, Jing
• Is Part Of: Journal of physics. Volume 1486:Issue 4(2020)
• Publication Date: 2020-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1486/4/042007
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Identifying radio anomalies is one of the main purposes of radio monitoring. The current radio anomaly signal identification is mainly finished manually by the radiops, using professional radio knowledge and their work experience. However, because the anomaly signal is hidden in the "massive" data, accompanied by a large amount of noise, and also data imbalance, the anomaly signal is difficult to find. In this paper, we combine the data unevenness processing method SMOTE and the (guorong: cluster detail) to identification the anomaly signal during radio monitoring. Experimental results show that our method can improve the efficiency of existing radio anomaly signal recognization. Moreover, our experiments also shows that data imbalance processing plays a key role in anomalyq signal recognition.