Deep fusion network based on two-stream CNN for radar target recognition

Usually radar target recognition methods only use a single type of radar data, such as synthetic aperture radar (SAR) or high-resolution range profile (HRRP). Compared with SAR, HRRP lacks the azimuth distribution information of the scattering center, but it has much looser imaging conditions than SAR. Both of them are important for radar target recognition. In fact, there is a correspondence between them. Therefore, in this paper, we propose an end-to-end fusion network, which can make full use of the different characteristics obtained from HRRP data and SAR images. The proposed network can automatically extract the features of HRRP and SAR data for fusion target recognition. It is a dual stream structure, which contains two separate feature extraction streams. One stream uses a 1D CNN to extract the complex features of HRRP data for full angle domain recognition, and the other uses a multi-scale 2D CNN to extract SAR features. An adaptive fusion module is designed in this paper for deeply fusing the two stream features and output the final recognition results. The contributions of this method mainly include: (1) A new end-to-end HRRP/SAR fusion network is proposed, and the experiment shows that our network significantly improves the recognition accuracy; (2) In HRRP feature extraction flow, we use a 1d-CNN, which can extract full angle features; (3) A multi-scale convolution neural network is used for SAR image feature extraction, which can solve the scale imbalance problem of SAR.