Recognition of Radar Emitter Signal Images Using encoding signal methods

We innovatively apply three different methods of encoding signal as 2-D plots to radar emitter signal recognition: Recurrence Plots (RP). Gramian Angular Field (GAF) and Markov Transition Field (MTF), thus the radar emitter signal recognition problem is converted into image processing problem. We build a 2-stage convolutional neural network (CNN) model to make use of its mature technology in the field of computer vision and image processing for signal recognition. These pipeline offers the following advantages: i) Encoding signal methods enable us to visualize certain aspects of the radar emitter signals through 2D images, and ii) CNN can automatically learn different levels of radar signal features and achieve a high recognition rate. In order to get a best encoding method, we compare the recognition accuracy of the three methods mentioned above, and combine the images encoded by the first two methods (RP and GAF) with high recognition accuracy into 2-channel RP-GAF images and get a better recognition rate. At last, we analyze the reasons why three encoding methods achieve different recognition rates.