Rotorcraft UAV Recognition Method Using Multi-Domain Feature Fusion Based on Millimeter-Wave Radar Sensor

To improve the recognition accuracy of rotorcraft unmanned aerial vehicles (UAVs) under millimeter-wave radar sensors, this paper proposes to use two millimeter-wave radars with the same wavelength band to detect UAVs. Based on the correlation of the UAV’s echo signals from two radars, we establish a fusion method of UAV echo signals detected by two radars based on the cross-correlation coefficient function method and develop a recognition algorithm for the echo signals of UAVs using multi-domain feature fusion measures. In the recognition algorithm, we process the fused echo signals of the UAV using the cross-correlation coefficient function; use time–frequency transformation to obtain time–frequency diagram, Cadence Velocity Diagram(CVD), and Cadence Frequency Spectrum (CFS) diagram; and then input the formed time–frequency diagram and CVD into the EfficientNet-B0-CBAM model and the CFS diagrams into the TCN-Transformer model and gain the multi-domain feature information of the UAV’s time–frequency, CVD, and CFS; lastly, we set up the multi-domain feature fusion extraction model to form a classification and recognition algorithm of the UAV’s echo signals. Based on our own built dataset of the UAV’s echoes, we conducted a comparative verification of the recognition algorithms of rotorcraft UAVs, and the results showed that the proposed method had an average improvement of 1.5% in terms of accuracy and precision.