Unmanned aerial vehicles (UAVs) provide a highly flexible platform for synthetic aperture radar (SAR), enabling efficient, high-quality imaging in remote sensing applications. In realistic imaging missions, regions of interest (ROIs) usually have different sizes and spatial distributions. While deploying SAR-UAVs with heterogeneous flight and imaging capabilities can improve mission time efficiency, realizing this improvement depends critically on task assignment and path planning. In this paper, the joint task assignment and path planning problem for heterogeneous SAR-UAVs in multi-region imaging missions is addressed. First, flight and imaging models of SAR-UAVs are established, and a constrained optimization problem is formulated to minimize the mission completion time. Then, an improved clustering strategy based on area-density and cost prediction (ADCP) is proposed to align ROI-dependent imaging workloads with heterogeneous SAR-UAV capabilities, thereby leveraging capability advantages and reducing the mission completion time. Finally, a discrete secretary bird optimization algorithm (DSBOA) is developed to generate feasible, high-quality paths. To accelerate convergence, UAV paths are encoded as waypoint sequences, and a mutation-based operator is introduced to update the population. Extensive Monte Carlo simulations show that the proposed approach consistently outperforms the baselines in mission completion time, demonstrating its effectiveness in improving time efficiency for multi-region SAR imaging missions. Ablation experiments further confirm the independent contributions of the proposed ADCP method and DSBOA algorithm.
Song et al. (Wed,) studied this question.