Projecte llegit
Títol: Improvement of a drone-based microwave payload based on RFI detection and mitigation
Estudiants que han llegit aquest projecte:
- MELIS PERELLÓ, ANDREU (data lectura: 27-10-2023)
- Cerca aquest projecte a Bibliotècnica
- MELIS PERELLÓ, ANDREU (data lectura: 27-10-2023)
- Cerca aquest projecte a Bibliotècnica
Director/a: PARK, HYUK
Departament: FIS
Títol: Improvement of a drone-based microwave payload based on RFI detection and mitigation
Data inici oferta: 06-02-2023 Data finalització oferta: 06-10-2023
Estudis d'assignació del projecte:
- DG ENG AERO/SIS TEL
Tipus: Individual | |
Lloc de realització: EETAC | |
Nom del segon director/a (UPC): Adriano Camps | |
Departament 2n director/a: | |
Paraules clau: | |
GNSS-R, RFI, Drone, Microwave Radiometer | |
Descripció del contingut i pla d'activitats: | |
Objectives:
This project pretends to improve the quality of data acquired by a drone-based microwave payload for soil moisture estimation by means of Radio Frequency Interference (RFI) detection and mitigation techniques. High mobility of drones allows to overfly certain regions which are likely to be a source of interference and therefore create a map of localized RFI. With this information, RFI sources may be avoided or diminished before acquiring data, from a Microwave Radiometer and a GNSS-R receiver, to which mitigation algorithms will be applied and therefore better soil moisture estimation results will be obtained. Also RFI impact on different GNSS bands can be studied in areas with high sensibility in terms of GNSS performance. The overall objective is to provide to NanoSat Lab, an aditional instrument for Earth Observation and a possible test bench for the coming payloads, as well as a tool to make scientific measurement of specific areas with higher spatial resolution than in-orbit systems. Methodology: To develop this project and comply with the objectives there will be 4 sections, each of them devoted to a part of the project. The first section has as an objective to study internal RFI, which are the interferences caused by active components that constitute the payload and that mostly affect the signal captured by the microwave radiometer. Each active component is isolated and shielded with aluminium to study how it contributes to internal RFI, which allows us to detect critical components and study the viability of the microwave radiometer, a passive sensor which is very susceptible to noise increase in the captured frequency band. In our case, L-band (1413 MHz) is captured as it has the highest sensitivity to soil moisture. The next section will be devoted to the creation of the RFI detection and mitigation (D/M) system. The acquired data will be analyzed with different D/M techniques which are classified depending on the studied domain. First, different scientific publications are studied to determine which algorithms may have better performance. Then, D/M will be implemented an validated by assessing its capacity for detecting a known jammer. Finally, mapping RFI detection on position information will permit us to create a map with RFI sources localized. The implementation of the algorithms for signal processing will be done in Python. The third part is intended to merge the data obtained from the RFI measuraments and data acquired by the microwave radiometer and the GNSS-R receiver in order to obtain better quality soil moisture estimation compared to the results obtained by each passive sensor itself. Also the possibility to integrate the three systems in one payload will be studied, as weight is the most critical parameter regarding drone limitations. And finally, after the development and testing of the system in the laboratory, its functionalities will be studied by means of measuring campaigns. The idea is to test the system in different environments to examine its behavior with high and low probability of RFI presence, as well as different humidity levels. These campaigns must have in consideration actual regulations regarding drone flight. Urban areas may be studied without the drone but with a ground vehicle or walking. Expected results: As for the expected results, the aim is to obtain soil moisture maps from radiometry and GNSS-R techniques merged with information obtained from the RFI system as well as maps that show possible sources of RFI in the areas where campaigns are carried out. As part of these processes evaluation of D/M techniques will be obtained and GNSS bands most likely to contain RFI. |
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Overview (resum en anglès): | |
The goal of this Final Degree Project is to develop a payload for a drone with an L-band radiometer and techniques for the detection and mitigation of interferences. It has been carried out in collaboration with NanoSat Lab, an initiative of the "Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona" dedicated to the design and development of nanosatellites with new technologies of Earth Observation by remote sensing.
This project is a continuation of the Final Grade Work titled "Development of a drone-based miniaturized Flexible Microwave Payload (FMPL) for GNSS-Reflectometry and L-band radiometry". This work aimed to develop the Flexible Microwave Payload Drone (FMPL-D), similar to the FMPL-1 payload used on the 3Cat-4 NanoSat Lab mission. FMPL-D integrates two passive remote sensing systems using microwaves: reflectometry Global Navigation Satellite System (GNSS) signals (GNSS-R) and microwave radiometry (MWR) in L-band. The impact of interferences on FMPL-D, particularly in MWR, has motivated the realization of this final degree thesis. The high mobility of the drone allows for results with a spatial resolution much higher than that obtained with satellites and the detection of possible interfering sources, which can already be unintentional or intentional (jammers). The thesis begins with the characterization of the MWR used in FMPL-D, which was determined to be impaired and unusable. As a solution, the RITA MWR schematic, developed at NanoSat Lab with collaboration from other partners, was adopted, which incorporates remote sensing and interference detection instruments. This memory describes the design, implementation and characterization of the new MWR. Two techniques are developed for the detection and mitigation of Radio-Frequency Interferences (RFI) by statistical analysis of the captured signal in two different domains. The results from kurtosis applied in the time domain and the spectral kurtosis applied in the time-frequency domain are used to compute several metrics for evaluating its effectiveness in the detection and mitigation of different types of RFI. The system is then analyzed through several laboratory experiments, including the study of MWR behavior in the absence of external interference and the results obtained with both techniques for detecting RFI. Finally, a functional test is performed on board a drone to qualify the system's performance and set targets for future work. In both cases, internal interference generated by a USB connection negatively affects the operation of the system. |