RayTracing Analysis and Simulator Design of Unmanned Aerial Vehicle Communication and Detection System in Urban Environment

Sammanfattning: In recent years, unmanned aerial vehicles (UAV), also called drones, have experienced a rapid increase, which leads to the concern of illegal use of them. Passive RF is one of the effective ways to detect drones by receiving drones’ communication signals. After receiving the signal from drones, one can utilize the prior knowledge of signal characteristics for identifying and locating the drones. The angle of arrival (AoA) measured by multiple passive RF sensors can be used for localization by triangulation. However, the accuracy of the AoA measured by the passive RF sensors is strongly affected by the environment. In particular in urban areas, the multipath effect is prominent due to the building blockage and complicated terrestrial conditions that introduce certain errors to the result. So the service provider of the sensors needs a tool to perform the environment analysis to understand the quality of the service. A fast tool that can simulate the sensor network and surrounding environment can offer a flexible solution to optimize the sensor coverage and indicate the blind zone of detection. Especially when the sensors are deployed on the mobile platform, such tool can significantly improve the defensive quality of the drone detection system by optimizing real-time deployment and indicating low observable areas. In order to plan the sensor locations and assess the performance after the deployment of the sensor at a fast speed, We propose a multipath-based model to calculate the AoA error. The model is able to utilize the input of geometrical information for simulating the AoA error within a region. In this thesis, we investigate the outdoor channel at 2.4GHz using the ray-tracing method as it is the most used channel for UAVs. Massive simulations have been carried out and real test flights have been conducted to evaluate the accuracy of the modeling. Both simulations and test flights are carried out in Kista center where buildings are from high-rises to one-floor houses with various heights. In the simulation, the AoA is obtained by MUltiple SIgnal Classification (MUSIC) algorithm. Test flights are conducted using an existing Software-defined radio (SDR) based RF sensor. We tried our best to carry out the same trajectories in both simulations and test flights to provide fair comparisons. The simulation results show that the multipath model can predict the trend of AoA error when the height changes, while not sufficient to predict the error when the 2D position changes. Thus, to more accurately characterize the signal transmission, it is essential to extend this thesis to include more detailed environmental information and adaption based on measurement.