UAV Swarm Types Identification with Micro-Doppler Signatures

This is the authors' abstract. We don't add key points for this paper.

With the widespread application of unmanned aerial vehicle (UAV) swarms in rescue operations, counter-terrorism, reconnaissance and other domains, the demand for effective swarm regulation and countermeasures has become increasingly urgent. Accurate swarm types identification serves as a fundamental prerequisite for achieving effective regulation and countermeasures. However, there has been minimal research on swarm types identification, and existing methods often face challenges such as complex feature extraction and low recognition accuracy. Aiming at the above problems, this paper proposes a method utilizing deep learning techniques to identify UAV swarm types based on micro-Doppler signatures. This approach leverages the unique characteristics of swarm micro-motions to enhance recognition accuracy. First, a radar echo signal model for UAV swarms is established, grounded in the principles of the micro-Doppler effect and electromagnetic scattering. Next, the micromotion features are derived from the echo signal using the short-time Fourier transform (STFT), thereby constructing the swarm feature dataset. Finally, a ResNet18 network model leveraging micro-Doppler features is developed to achieve accurate swarm types identification. Simulation results indicate that ResNet18 achieves the best performance in UAV swarm types identification compared to widely used target recognition network models such as AlexNet, GoogLeNet and ResNet34. Furthermore, the proposed method requires fewer network parameters and lower computational overhead, while exhibiting strong robustness to noise. Notably, even at a signal-to-noise ratio (SNR) of 0 dB, the recognition accuracy for the six different swarm target types remains above 90%.