Lightweight Self-Supervised Recognition of Small-Sample Ships Using Micro-Doppler Signatures and UAV-Based UWB Radar

The continuous advancement of shipbuilding technology has enabled the further development of sea transport and naval vessels, which also poses significant challenges to navigation safety. Accurate recognition of different types of ships is crucial. In this study, three typical ships, namely a destroyer, cruise ship, and aircraft carrier, were taken as examples. The micro-Doppler effect of these ships was analyzed using a unmanned aerial vehicle (UAV)-based ultrawideband (UWB) radar. Echo signal models were established and compared with measured Doppler signals. Furthermore, a lightweight self-supervised learning network was proposed to achieve accurate recognition of small-sample labeled ship targets. The neural network architecture, including specific layers and parameters, was introduced. To enhance the micro-Doppler features, the 1-D signals were transformed into 2-D images, thereby converting the ship recognition problem into an image classification problem. The experimental results demonstrate that the proposed network achieves high recognition accuracy under different situations. When evaluated on measured data, the overall recognition rate reaches 97.50%. Moreover, even in challenging scenarios with a low signal-to-clutter ratio (SCR) of −10 dB, the overall recognition rate for simulated data still exceeds 90%, demonstrating the effectiveness and advantages of the proposed network.