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Projecte llegit

Títol: Improvement of a drone-based microwave payload based on RFI detection and mitigation

Estudiants que han llegit aquest projecte:

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:
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:

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.


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
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.

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