UAV Trajectory Planning for Radar Deployment Assessment

Optimal radar deployment is an important element in designing surveillance and monitoring systems for protected areas, particularly in applications involving low-altitude unmanned aerial vehicles (UAVs). Assessing a given radar configuration requires identifying potential UAV trajectories that exploit regions of reduced radar coverage and may therefore result in delayed detection. This paper presents a trajectory-planning framework for radar deployment assessment based on three complementary approaches: Parallel Quick Rapidly-exploring Random Tree* (PQRRT*), Fast Marching Method (FMM), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The radar coverage area is represented by a discretized spatial grid in which each cell is characterized by the probability of detecting a UAV with a specified radar cross-section at a given flight altitude. The trajectory-planning problem is formulated to identify routes that minimize the cumulative probability of detection before reaching a predefined target region. The considered algorithms are adapted to operate in such continuous radar-coverage fields rather than only in environments containing discrete obstacles. In addition, the proposed PQRRT* algorithm employs parallel computation and non-blocking data structures, enabling efficient trajectory generation over large-scale areas represented by grids containing millions of cells. Numerical experiments demonstrate that PQRRT* and FMM efficiently generate multiple UAV trajectories in large-scale radar-coverage scenarios, whereas CMA-ES requires substantially higher computational effort for more complex optimization settings. The generated trajectories enable the identification of potential coverage gaps and provide quantitative information for assessing alternative radar configurations. The results demonstrate that the proposed framework can support radar-coverage assessment and assist in improving radar deployment for UAV monitoring and protection of critical areas.