Radio Anomaly Signal Recognition Methods Based on Clustering
Shengyang Li, Jianxi Lin, Bin Tian, Shen Li, Guorong Cui, Jing He · Journal of Physics Conference Series · 2020
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.