Recognition of Micro-Motion Space Targets Based on Attention-Augmented Cross-Modal Feature Fusion Recognition Network

Narrowband and wideband waveforms are usually adopted simultaneously during the observation of micro-motion space targets by inverse synthetic aperture radar (ISAR), which can collect rich multimodal information in the time-Doppler, time-range, and range-instantaneous-Doppler (RID) domains. In order to exploit the electromagnetic scattering, shape, structure, and motion characteristics, this article proposes an attention-augmented cross-modal feature fusion recognition network (ACM-FR Net). First, the ACM-FR Net adopts a convolutional neural network (CNN) to extract initial feature vectors from joint time-frequency (JTF) image, high-resolution range profiles (HRRPs), and RID image. Then, it transforms the feature vectors of the three modalities into feature sequences. Finally, it achieves interactive feature fusion by implementing ACM feature fusion. In the four-category micro-motion space target recognition experiments, the proposed ACM-FR Net has demonstrated high accuracy and noise robustness.