Fast prediction method for dynamic RCS of rotary wing small UAVs

In the military field, studying the characteristics of radar cross section (RCS) of multi-rotor small UAVs is of great significance for UAV penetration and high-value target feature simulation. By studying the variation law of dynamic RCS of multi-rotor small UAV and combining deep learning technology, a dynamic RCS rapid estimation method based on Long Short-Term Memory (LSTM) is proposed, and its prediction effect is simulated and verified. This method can estimate the RCS of UAVs within a certain time range in the future during the flight of multi-rotor small UAVs, and provide data support for the optimal waveform design and target recognition of "low, slow and small" target detection, and then realize the accurate interception of multi-rotor small UAVs.