Abstract:

Feature extraction is one of the key technologies for high resolution range profile (HRRP) based radar target recognition. The traditional artificial feature extraction algorithm, which only uses shallow structure features, can not effectively solve the pose sensitivity problem, which limits the generalization of target recognition methods. Thus, a target recognition method based on deep learning is proposed, and the application boundary conditions of this method through detailed pose angle performance test is analyzed. By constructing a convolutional neural network (CNN) model suitable for processing HRRP, the deep-seated pose insensitive attributes of targets are fully explored, and high-precision target recognition is completed. Based on the measured data, the experimental results show that the proposed method has certain anti pose sensitivity characteristics, and the boundary condition analysis can provide guidance for the engineering application of the method.

Key words: radar target recognition, high resolution range profile (HRRP), pose sensitivity, deep learning, convolutional neural network (CNN)