This project combines GNSS-R technology and UAVs in the context of the aerial remote sensing field, from the design of the assembly of the UAVs and GNSS-R to the execution of the different tests, as well as the data collection and analysis. As it will be seen in the document, this analysis has allowed to improve the original design of the system. The remote sensing technology based on GNSS reflected signals is simply called GNSS-R, and the greatest advantage of UAV as a mounting platform is the low cost, high system availability, and fast installation. Thus the main task of this project is to experimentally verify the possibility of CTTC's own developed GNSS-R working with UAVs. In this report, first I explain some basic principles of GNSS and GNSS-R, as they are important technical backgrounds for the whole project. Secondly, I describe the application of the RTKPOST software, which is an important processing and analyzing tool in the entire project, mainly describing the positioning methods used in the different processing modes in RTKPOST, also the parameter needed for analysis. Then, the design of the UAV-GNSS-R assembly is discussed, with different options based on the characteristics of the UAV, such as weight, payload space, and so on. The whole experimental process is also explained, and all the test data are analyzed and processed by using the Static mode or Kinematic mode in RTKPOST, and the data results are analyzed and compared in terms of satellite visibility, signal-to-noise ratio, elevation angle, Standard Deviation and Root Mean Square. The experimental process is roughly divided into three parts. The initial verification test is used to collect signal data as a basis, through which it is found that drone interferes with GNSS-R to a greater extent, and that the reflector antenna cannot receive the signal if the drone is placed too close to the ground. The second part of the experiment is a stationary test, where the UAV is placed on a tripod, and it's found that there is a difference in accuracy when the UAV is working on different land surfaces, which a higher accuracy can be obtained on rock than on grassy field. It is also found that the interference problem can be mitigated when the receiver is placed farther away from the UAV body. In the last part of the flight test, the optimization of the interference problem is further confirmed by the comparison of the two flight tests, and the positioning error is reduced from about 50m to 6m. At the end of the report, suggestions are given for the future development of this project, as it is clear that the meter-level accuracy does not meet the needs of many applications, and that the integration of the UAS and GNSS-R needs to be further optimized to reduce interference problems and thus achieve higher accuracy positioning.
