Abstract: To address the issue of low perception quality in DFRC base stations due to obstacles, an architecture is designed where a drone (UAV) carries an intelligent reflecting surface (RIS) to expand the base station's sensing range. Based on the communication quality and system energy consumption constraint models, a multi-variable collaborative optimization model for the base station's active beamforming, reflection surface coefficient matrix, and drone height is constructed. Firstly, the MM (majorization-minimization) algorithm is applied to construct the lower bound expression of the radar signal-to-interference-plus-noise ratio (SINR), and the optimal beamforming matrix is solved through semi-definite programming. Secondly, within the MM framework, the semi-definite relaxation method is combined to solve the high-dimensional operation problem introduced by the RIS. Finally, the successive convex approximation method is used to solve for the optimal height of the drone. Simulation results show that by alternately optimizing the three variables, the radar SINR can be improved by 30-60 dB, and the beam energy in the target direction is highly concentrated. Compared with the traditional single-variable optimization scheme, the proposed multi-variable joint optimization algorithm converges faster and has better communication and sensing performance.