Situation Awareness Method and Simulation Test of UAVs and Observer Wards Air-Ground Cooperation

Target localization and situational awareness of air-ground cooperation have become an important trend and inevitable choice in the field of unmanned equipment and swarm intelligence. The mode framework and related algorithms of air-ground cooperation between UAV and ground vehicles, ground personnel, unmanned vehicles, etc. have also become a research hotspot in the field of artificial intelligence. The purpose of this study is to explore a cheap, novel and efficient UAV air-ground cooperation mode. Specifically, this study proposes a “UAV-Observer Ward” air-ground cooperation mode, and creatively designs a new type of equipment called “observer ward” (small volume long-distance wireless human body detector), and also designs a flexible cooperation framework to let the observer ward discover and guide the UAV s to intercept the detected target. “Target Interception Rate” is creatively introduced as the evaluation factor of the equipment efficiency of the perception system. Based on this, our research proposes an air-ground cooperation situational awareness algorithm that considering information transmission. The algorithm senses and predicts the moving trajectory of the intrusion target through the communication between several observer wards, and guides the UAV cluster to intercept the intrusion target more intelligently. This study uses simulation tests based on AnyLogic software to verify the superiority of the “UAV-Observer Ward” air-ground cooperation mode. The experimental results show that the “target interception rate” of the new air-ground cooperation mode is 2.87, 2.81, 3.04, 4.37 and 4.31 times higher than that of the traditional mode, respectively, at the target intrusion speeds of 10rlh, 20r/h, 30r/h, 60r/h and 90r/h.