Optimized Radar Waveform Parameter Design for Small Drone Detection Based on Echo Modeling and Experimental Analysis

Monitoring small drones is significant for security requirements, but it is challenging because of small drones' low radar cross section (RCS) and hovering ability. In order to analyze the effect of radar waveform on the echo spectra and optimize the waveform design, an analytical expression of the rotor's temporal RCS is desired. Integral model, a widely used radar echo model of the rotor's temporal RCS, does not involve the electromagnetic scattering, thus it cannot be applied to all frequency bands. The method of moments (MoM) is a strictly numerical method based on the Maxwell's equations, but cannot obtain the analytical expression. Hence, this paper proposes a new method combing the integral model and MoM to model the rotor's temporal RCS in very high frequency (VHF) band. The linear frequency modulation (LFM) signal which has a high Doppler tolerance and a large gain bandwidth product is adopted in optimizing waveform parameters design. Based on the new method, the analytical expression of LFM echo is also derived. Moreover, the spectral spread over range and Doppler dimension caused by the rotating rotors is analyzed in detail. A criterion for optimizing the LFM waveform parameters for small drone detection is presented. Field experiments confirm the validity of the echo model and waveform parameters optimization criterion, and the full Acrylonitrile Butadiene Styrene small drone is successfully detected in VHF band. In addition, experiments are implemented to verify the applicability of the echo model and waveform parameters optimization method to other drones with different size.