Spatiotemporal optimization for communication-navigation-sensing collaborated emergency monitoring

In the event of disaster-related disruptions to public networks, an air-ground communication-navigation-sensing (CNS) ad-hoc network (ANET) can effectively support emergency response operations by ensuring communication, navigation and sensing capabilities. However, during extreme events such as typhoons and torrential rain, flight operations become impossible for aircraft. Therefore, a critical issue that requires immediate investigation is how to effectively provide communication, navigation, and sensing assistance to emergency regions using only ground-based CNS nodes. This paper presents a communication-navigation-sensing spatiotemporal cooperative optimization algorithm based on ResNet-DDPG. It aims to achieve spatiotemporal cooperative optimization of ground CNS nodes in the absence of aerial relay ad hoc network nodes (RANET nodes). This work has three main components: (1) a neural network-based geospatial fitting model for ANET communication, (2) a ResNet based spatiotemporal feature and CNS node fusion encoding method for state space construction for reinforcement learning, and (3) a deep reinforcement learning algorithm called ResNet-DDPG for deploying ground ANET nodes dynamically. Experimental results indicate that, compared to similar approaches, this method significantly improves communication rate optimization and deployment site effectiveness. This study supports emergency response by utilizing ground ANET, which is crucial for disaster emergency response, command scheduling, and life rescue in extreme disaster scenarios.