A Signal Source Detection Method Based On The Fusion Of Drone Vision And Wireless Signals

In the management of wireless security,wireless signal source detection technology can be used to locate targets and detect fake base stations in communication networks,which is of great significance for ensuring wireless security and preventing electromagnetic radiation pollution.In applications such as post-disaster relief and wildlife observation,wireless signal source detection can help locate targets carrying electronic devices.Traditional signal source detection technology locates signal sources through active or passive means,usually using methods such as time difference of arrival and frequency difference of arrival.It requires the use of complex antenna arrays and human collaboration with wireless direction finders for locating.Synchronization of equipment timing is also required.The popularity of drones has provided new directions for many studies.By carrying various sensor devices on drones,many tasks previously impossible due to mobility restrictions can now be achieved.By mounting cameras and software-defined radio devices on drones and relying on edge computing,ground images can be obtained and corresponding objects can be identified.With binocular cameras,further visual depth estimates of objects can be obtained.By mounting software-defined radio devices on drones,wireless signals from ground signal sources can be obtained,and the objects corresponding to these signals can be determined.This paper focuses on the scenario where the ground signal source is interfered with by visually camouflaged objects and proposes a target detection method based on the fusion of drone vision and wireless signals to identify the signal source object and determine its precise location.The main research work of this paper is as follows:(1)A signal source identification method based on the fusion of drone vision and wireless signals is proposed,which uses a multi-source fusion discriminant method to identify the real transmitting target in the environment.Firstly,under the unknown environment parameters,the maximum likelihood estimation of the signal source’s transmission power,environmental attenuation coefficient and other unknown parameters is calculated iteratively,and then the signal source is finally discriminated based on the minimum mean square error.Simulation experiments show that the algorithm has the characteristics of high accuracy and low computational cost.(2)A positioning method based on the fusion of drone vision and wireless signals is proposed,and a multi-drone path planning method is given to optimize the positioning accuracy of the signal source.A Kalman filter is used to fuse information as input,iteratively optimizing the estimated position,and planning the drone’s path by maximizing the determinant value of the Fisher information matrix.Constraints such as safety distance and communication distance between drones and obstacle avoidance are considered in the drone path planning.Simulation experiments show that the algorithm can effectively plan paths and achieve higher accuracy in target positioning than using a single signal only.(3)A system for detecting fake base stations on drones is designed and implemented,including hardware and software.The requirements of the system are analyzed,and the hardware is selected based on the current experimental needs and the scalability of the platform in the future.Software for each part is written,including reading wireless signal data from HackRF devices and data transmission modules on the drone’s onboard Raspberry Pi.Finally,a field test is designed,and the results show that the system works well and can correctly identify the target.