DeepASTC:Antenna Scan Type Classification Using Deep Learning

In this work, we propose a new method which we call DeepASTC, for antenna scanning type classification in Electronic Warfare Systems. DeepASTC is a deep neural network composed of LSTMs. Amplitude patterns of the deinterleaved radar pulses are fed into our network, and the corresponding scanning type is automatically obtained. DeepASTC and the Multi-class Support Vector Machine (SVM) based classifier method are compared. It is observed that the proposed DeepASTC is able to achieve 93.8% correct classification rate on average, whereas the corresponding rate for the Multiclass SVM method is 86.3%. Conducted experiments show that, the proposed DeepASTC performs successfully on the synthetic data sets.