Abstract: With the widespread adoption of unmanned aerial vehicle （UAV） technology， the threat posed by UAVs to low-altitude safety has become increasingly prominent. Effective radar detection and identification of UAVs has thus become critical. The multicomponent UAV echo signal is primarily composed of relatively stable main body echoes （from the airframe） and time-varying micro-motion component echoes （from rotors and propellers）. The micro-Doppler effect generated by these moving components is a key feature in target identification. However， in inverse synthetic aperture radar imaging， it degrades the clarity of the main body image. Conversely， during feature extraction， the main body echoes obscure the micro-Doppler information. Therefore， achieving effective separation between the main body and moving component echoes is one of the core challenges in the effective detection and identification of UAV targets. A Hankel matrix low-rank sparse decomposition is proposed for the echo separation problem of the UAV main body and its micro-motion components. First， the subject and micro-motion signal separation problem is modeled as a low-rank sparse matrix decomposition problem. Second， to enhance the robustness of the algorithm， the factor group-sparse regularization method is employed to relax the rank function in the low-rank sparse decomposition model. This approach is then combined with the method of linearized alternating direction of multiples to solve the model， whereby the echo signals are separated from the narrowband radar target subject and micro-motion components. Finally， the effectiveness and robustness of the proposed method are verified through simulations and measured data processing.