Multimodal Fusion and Dynamic Resource Optimization for Robust Cooperative Localization of Low-Cost UAVs

To overcome the challenges of low positioning accuracy and inefficient resource utilization in cooperative target localization by unmanned aerial vehicles (UAVs) in complex environments, this paper presents a cooperative localization algorithm that integrates multimodal data fusion with dynamic resource optimization. By leveraging a cross-modal attention mechanism, the algorithm effectively combines complementary information from visual, radar, and lidar sensors, thereby enhancing localization robustness under occlusions, poor illumination, and adverse weather conditions. Furthermore, a real-time resource scheduling model based on integer linear programming is introduced to dynamically allocate computational and communication resources, which mitigates node overload and minimizes resource waste. Experimental evaluations in scenarios including maritime search and rescue, urban occlusions, and dynamic resource fluctuations show that the proposed algorithm achieves significant improvements in positioning accuracy, resource efficiency, and fault recovery, demonstrating strong potential for applications in complex tasks, demonstrating its potential as a viable solution for low-cost UAV swarm applications in complex environments.