GAF Representation of Millimeter Wave Drone RCS and Drone Classification Method Based on Deep Fusion Network Using ResNet

Abstract

In recent years, with the rapid increase in the number of drones, the abuse of drones for activities such as terrorist attacks is bound to pose a threat to public safety and privacy. Therefore, it is very important to identify and classify them. Millimeter-wave (MMW) radar provides an effective means to detect drones. And with the development of B5G/6G technologies, a large number of MMW base stations will be deployed in cities in the future. These MMW base stations can be used as MMW radar through simple modification, which greatly saves the detection cost. Radar cross section, as a common data of radar, can provide a data source for drone recognition and classification. Therefore, using the publicly available radar cross section (RCS) data set of drones in the MMW band. We first encode the RCS series into a 2D Gramian angular field (GAF) representation and design a 2D ResNet-10 to classify them. Second, we propose a deep fusion network, which can be used as an RTR with RCS as the information source. The Experimental results show that 2D ResNet-10 is also effective in classifying GAF representations and its time consumption is less than 2D ResNet-18. Compared with other RCS-based classification methods, the performance of deep fusion network is the best.