Radar Micro-Doppler Signatures Model Simulation and Feature Extraction of Three Typical LSS Targets

Small unmanned aerial vehicles (UAVs) detection and discrimination has become increasingly significant due to its extensive application and potential threats. It can be divided into two steps: the first is to distinguish man-made and natural targets and then further characterize UAVs detailed information. Detecting high-speed units of targets using micro-Doppler signatures (mDs) can recognize UAVs from birds quickly. In this paper, pay emphasis on discussing the different evolution of mDs of three typical LSS targets (Quadcopter, Helicopter and Bird) in short-time and long-time integration. In short time interval, instantaneous mDs accord to sinusoidal modulation. Convert spectrum into time-frequency spectrogram, cadence velocity diagram (CVD), sum of cadence velocity diagram (SCVD) and singular value decomposition (SVD) to extract micro-Doppler features accurately. In long-time integration, utilize pulse compression and long-time integration algorithm to obtain single frame Doppler modulation spectrum and propose a regularized log-Doppler spectrogram, enlarging the weak side frequencies and clarify the spectrum width and the number of spectral lines, from which can extract modulation repetition frequency and single bandwidth to distinguish UAVs from birds and estimate more detailed information about the target.