Strategic Optimal Control of Multi-Camera Trajectories for UAV Capture Using Entropy and Coverage Approaches

Abstract

The widespread use of unmanned aerial vehicles (UAVs) has heightened the demand for effective UAV monitoring, particularly in protected areas. Current learning-based detection systems depend heavily on camera sensor ability to capture UAVs in surveillance areas; however, advanced camera control methods remain limited. This paper proposes determining multi-camera trajectories that maximize UAV capture probability, ensuring UAVs remain within the camera field of view for further analysis, such as detection methods from camera-shot images. For this purpose, stochastic modeling is considered in the control framework for optimizing surveillance camera trajectories to enhance the probability of capturing UAVs. Key control parameters are derived through classical probability evaluations of the model with maximizing the entropy and covering trajectory-based strategies are applied. The reliability of stochastic system modeling is empirically validated through comprehensive computational experiments. These findings demonstrate the model potential to enhance UAV capture rates through optimized camera trajectories and coverage efficiency, paving the way for future advancements in real-environment applications.