Seok‐Jun HongYearn-Gui YiJeil JoSang‐Gil LeeBo-Seok Seo
In this paper, we propose a classification method for radar signals depending on the type of threat by applying machine learning to parameter data of radar signals . Currently, the army uses a library of mapping relations between the parameters and the types of threat to recognize threat signals. This approach has certain limitations when classifying signals and recognizing new types of threat or types of threat that do not exist in the current libraries. In this paper, we propose an automatic radar signal classification method depending on the type of threat that uses only parameter data without a library. A convolutional neural network is used as the classifier and machine learning is applied to train the classifier. The proposed method does not use a library, and hence, can classify threat signals that are new or do not exist in the current library.
Yinbing ZhangXinzheng LvWei Min
Xianpeng MengShang Chao-xuanJian DongXiongjun FuPing Lang
Kuiyu ChenJingyi ZhangSi ChenShuning ZhangHuichang Zhao
Cesur KarabacakSevgi Zübeyde GürbüzAli Cafer Gürbüz