Vehicle Recognition Based on Carrier-Free UWB Radars Using Contrastive Multi-View Learning

A novel carrier-free ultrawideband (UWB) radar-based automatic vehicle recognition system is reported. We provide complete transceiver units, design a UWB Vivaldi antenna with centimeter-scale dimensions and bandwidth from 0.89 to 5.02 GHz, and propose the multi-view-based contrastive learning algorithm for target recognition. Simulated results of the antenna are compared with measured results and are shown to be in good agreement. Though target recognition benefits from large, curated labeled data, its application to problems with limited annotated data remains a challenge. In addition, the target-aspect sensitive issue also impacts the performance of models. For that, we combine contrastive learning with unlabeled multi-view data to learn target-aspect-invariant representations. The experiments on the different datasets demonstrate the effectiveness and generalization of our method.