Abstract: To address the issue of low perception quality in dual-function radar-communication(DFRC) base stations during integrated sensing and communication (ISAC) applications due to obstacles, an ISAC extension architecture is designed where an unmanned aerial vehicle (UAV) carries an intelligent reflecting surface (RIS) to expand the sensing range of the base station. Based on the communication quality and system energy consumption constraint models, a multi-variable collaborative optimization model for the active beamforming of the base station, reflection surface coefficient matrix, and drone height is constructed. Firstly, the majorization-minimization (MM) 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. Compared with the traditional single-variable optimization scheme, the proposed multi-variable joint optimization algorithm converges faster and has better communication and sensing performance.