Sensing ground users is recognized as one of the key operations in urban air mobility (UAM) systems, ensuring high sensing performance is crucial for providing accurate data for various urban tasks. In this paper, we focus on maximizing the radar sensing rate for each user by jointly optimizing the UAM aircraft’s flight trajectory, speed, and acceleration. The challenge lies in addressing the complex non-convex nature of the problem, which includes non-convex constraints that are difficult to solve using standard optimization techniques. To overcome this, we utilize methods such as successive convex approximation and Taylor series expansion, which enable us to transform the non-convex constraints into convex ones, making the problem solvable with convex optimization solvers. Simulation results validate the effectiveness of the proposed approach, demonstrating that it not only enhances the sensing rate but also exhibits robust performance across various flight conditions.