The goal of this project is to create an algorithm based on cooperative placement and multi-detection to address the observation and routing problems related to drones. The technique enables multi-detection technology observation of the surrounding environment and high-precision drone positioning using collaborative positioning technology. To maximize the fused data and improve the accuracy of the routing and observation results, a state-space model is applied. The algorithm proposed in the research was validated using an investigational test for UAVs. The Dijkstra method determines the shortest path between two points based on the number of nodes that are utilized. A fresh measurement was obtained using an interpolation method to determine the drone’s position each time. The probabilistic data association, which is centralized, and the fused extended Kalman filter, which are shown in this work, demonstrate the air-to-ground detection capabilities offered by two aerial sensors that identify an unstable ground target. Simulations present the observation and routing algorithm developed during this study which significantly increases positioning accuracy with communication efficiency and reduces collision possibilities caused by vehicles accidentally entering within obstacle areas and increases observation accuracy up to 96.8%.