Integrated Processing of Radar Detection and Classification for Moving Target via Time-frequency Graph and CNN Learning

As the main means of target detection and surveillance, radar is widely used in the field of public safety and defense security. However, due to the complex environment and the complex motion characteristics of the target, the target echo is extremely weak and has low observability, which makes it difficult for the radar to detect the moving target in the clutter background [1]. The low-observable moving target detection in clutter has become a key constraint technology and a worldwide problem [2]. The traditional moving target detection (MTD) method is only applicable for uniformly moving targets. For the maneuvering targets under strong clutter and interference conditions, radar echoes will not meet the requirements of traditional signal processing. Moreover, the classification for different motions is difficult and not general under complex environment. There is an urgent need to develop and study adaptive, general and efficient methods for moving target detection and classification.