Abstract
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 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, namely ACM-FR Net. Firstly, the ACM-FR Net adopts convolution neural network (CNN) to extract initial feature vectors from joint time-frequency (JTF) image, high resolution range profiles (HRRPs), and range-instantaneous-Doppler (RID) image, respectively. Then, it transforms the feature vectors of the three modalities into feature sequences. Finally, it achieves interactive feature fusion by implementing attention-augmented cross-modal feature fusion. In the four-category micro-motion space targets recognition experiments, the proposed ACM-FR Net has demonstrated high accuracy and noise robustness.
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