Bistatic SAR Automatic Target Recognition With Multichannel Multiview Feature Fusion Network

Abstract

Bistatic synthetic aperture radar (SAR) with spatially separated transmitter (TX) and receiver (RX) is advantageous over monostatic SAR systems in trajectory flexibility and antistealth/antijamming capability. On the other hand, since bistatic SAR imaging involves more technical complexities and incurs higher cost, the research in the field of bistatic automatic target recognition (ATR) has been mainly relying on simulated SAR imagery. Reckoning with the lack of supporting database in the public domain, the researchers at Nanjing University of Aeronautics and Astronautics (NUAA) constructed a proprietary bistatic SAR database featuring multiple types of representative military vehicles with the self-developed miniSAR system. Moreover, a multichannel multiview feature fusion network (MMFFN) is devised by incorporating the vision transformer (ViT). The simulation results show that the proposed MMFFN offers a classification accuracy improvement of 4.86%–16.63% over the baseline network (i.e., the plain ViT) in a series of experiments featuring small-to-large observation angle deviations between the training and test data.