Dear editor, The micro-Doppler modulation caused by the rotors on an unmanned aerial vehicle (UAV) can be employed to recognize the UAV. However, several rotors on a UAV, as well as multiple targets at the same scene, lead to a multicomponent micro-Doppler signal in the radar echo. Conventional signal decomposition is an important method for extraction of the features from micro-Doppler signals [1], although it is incapable of separating characteristic curves that are overlapping [2] in the time-frequency (TF) plane in the case of multicomponent micro-motion, which can lead to the poor precision of parameter estimation. Thus, an effective approach to multicomponent micro-Doppler signal decomposition and parameter estimation needs to be developed. The TF transformassisted image pattern recognition, which is a nonparametric TF transform, has been proposed to estimate the parameters of targets with micromotion [3]. However, in cases where the motion of the UAV does not match the non-stationary signal model, the TF transform causes poor energy concentration and cross-term interference problems, resulting in inaccurate parameter estimation. Providing that the chirplet transform is applied, the parametric TF transform [4, 5] can effectively improve the energy concentration in TF representation for a linear frequency modulation (LFM) signal. However, because of the mismatch of the kernel function, the micro-Doppler signal of the UAV, especially the multicomponent signal, is a sine FM instead of an LFM, so the concentration of each component cannot be optimized simultaneously. In addition, because of the mutual occlusion between rotors in real situations, the parametric TF transform leads to fracture and discontinuity phenomena in the TF curve. Here, we focus on signal decomposition and high-precision parameter estimation of the micro-motion target under multicomponent and occlusion effects. A multicomponent kernel function estimation method for a parametric TF transform is proposed, in which the Hough transform is combined with Fourier series expansion. The effectiveness is verified by theoretical analysis and simulations. For the moving rotor, the instantaneous frequency is in the form of a sine curve [6], supposing that the TF curves for each component with micro-motion are ˆfm-D (t), which can be expanded using a Fourier series as