Kriging-Based 3-D Spectrum Awareness for Radio Dynamic Zones Using Aerial Spectrum Sensors

Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this paper, we consider a 3D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3D Kriging interpolation technique that uses realistic 3D correlation models of the signal power extracted from extensive measurements carried out at the NSF AERPAW platform. Using C-Band signal measurements by a UAV at altitudes between 30 m-110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss

Autonomous Obstacle Collision Avoidance System for UAVs in rescue operations

The Unmanned Aerial Vehicles (UAV) and its applications are growing for both civilian and military purposes. The operability of an UAV proved that some tasks and operations can be done easily and at a good cost-efficiency ratio. Nowadays, an UAV can perform autonomous tasks, by using waypoint mission navigation using a GPS sensor. These autonomous tasks are also called missions. It is very useful to certain UAV applications, such as meteorology, vigilance systems, agriculture, environment mapping and search and rescue operations. One of the biggest problems that an UAV faces is the possibility of collision with other objects in the flight area. This can cause damage to surrounding area structures, humans or the UAV itself. To avoid this, an algorithm was developed and implemented in order to prevent UAV collision with other objects. “Sense and Avoid” algorithm was developed as a system for UAVs to avoid objects in collision course. This algorithm uses a laser distance sensor called LiDAR (Light Detection and Ranging), to detect objects facing the UAV in mid-flights. This light sensor is connected to an on-board hardware, Pixhawk’s flight controller, which interfaces its communications with another hardware: Raspberry Pi. Communications between Ground Control Station or RC controller are made via Wi-Fi telemetry or Radio telemetry. “Sense and Avoid” algorithm has two different modes: “Brake” and “Avoid and Continue”. These modes operate in different controlling methods. “Brake” mode is used to prevent UAV collisions with objects when controlled by a human operator that is using a RC controller. “Avoid and Continue” mode works on UAV’s autonomous modes, avoiding collision with objects in sight and proceeding with the ongoing mission. In this dissertation, some tests were made in order to evaluate the “Sense and Avoid” algorithm’s overall performance. These tests were done in two different environments: A 3D simulated environment and a real outdoor environment. Both modes worked successfully on a simulated 3D environment, and “Brake” mode on a real outdoor, proving its concepts.Os veículos aéreos não tripulados (UAV) e as suas aplicações estão cada vez mais a ser utilizadas para fins civis e militares. A operacionalidade de um UAV provou que algumas tarefas e operações podem ser feitas facilmente e com uma boa relação de custo-benefício. Hoje em dia, um UAV pode executar tarefas autonomamente, usando navegação por waypoints e um sensor de GPS. Essas tarefas autónomas também são designadas de missões. As missões autónomas poderão ser usadas para diversos propósitos, tais como na meteorologia, sistemas de vigilância, agricultura, mapeamento de áreas e operações de busca e salvamento. Um dos maiores problemas que um UAV enfrenta é a possibilidade de colisão com outros objetos na área, podendo causar danos às estruturas envolventes, aos seres humanos ou ao próprio UAV. Para evitar tais ocorrências, foi desenvolvido e implementado um algoritmo para evitar a colisão de um UAV com outros objetos. O algoritmo "Sense and Avoid" foi desenvolvido como um sistema para UAVs de modo a evitar objetos em rota de colisão. Este algoritmo utiliza um sensor de distância a laser chamado LiDAR (Light Detection and Ranging), para detetar objetos que estão em frente do UAV. Este sensor é ligado a um hardware de bordo, a controladora de voo Pixhawk, que realiza as suas comunicações com outro hardware complementar: o Raspberry Pi. As comunicações entre a estação de controlo ou o operador de comando RC são feitas via telemetria Wi-Fi ou telemetria por rádio. O algoritmo "Sense and Avoid" tem dois modos diferentes: o modo "Brake" e modo "Avoid and Continue". Estes modos operam em diferentes métodos de controlo do UAV. O modo "Brake" é usado para evitar colisões com objetos quando controlado via controlador RC por um operador humano. O modo "Avoid and Continue" funciona nos modos de voo autónomos do UAV, evitando colisões com objetos à vista e prosseguindo com a missão em curso. Nesta dissertação, alguns testes foram realizados para avaliar o desempenho geral do algoritmo "Sense and Avoid". Estes testes foram realizados em dois ambientes diferentes: um ambiente de simulação em 3D e um ambiente ao ar livre. Ambos os modos obtiveram funcionaram com sucesso no ambiente de simulação 3D e o mode “Brake” no ambiente real, provando os seus conceitos

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

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Development of high-precision snow mapping tools for Arctic environments

Le manteau neigeux varie grandement dans le temps et l’espace, il faut donc de nombreux points d’observation pour le décrire précisément et ponctuellement, ce qui permet de valider et d’améliorer la modélisation de la neige et les applications en télédétection. L’analyse traditionnelle par des coupes de neige dévoile des détails pointus sur l’état de la neige à un endroit et un moment précis, mais est une méthode chronophage à laquelle la distribution dans le temps et l’espace font défaut. À l’opposé sur la fourchette de la précision, on retrouve les solutions orbitales qui couvrent la surface de la Terre à intervalles réguliers, mais à plus faible résolution. Dans l’optique de recueillir efficacement des données spatiales sur la neige durant les campagnes de terrain, nous avons développé sur mesure un système d’aéronef télépiloté (RPAS) qui fournit des cartes d’épaisseur de neige pour quelques centaines de mètres carrés, selon la méthode Structure from motion (SfM). Notre RPAS peut voler dans des températures extrêmement froides, au contraire des autres systèmes sur le marché. Il atteint une résolution horizontale de 6 cm et un écart-type d’épaisseur de neige de 39 % sans végétation (48,5 % avec végétation). Comme la méthode SfM ne permet pas de distinguer les différentes couches de neige, j’ai développé un algorithme pour un radar à onde continue à modulation de fréquence (FM-CW) qui permet de distinguer les deux couches principales de neige que l’on retrouve régulièrement en Arctique : le givre de profondeur et la plaque à vent. Les distinguer est crucial puisque les caractéristiques différentes des couches de neige font varier la quantité d’eau disponible pour l’écosystème lors de la fonte. Selon les conditions sur place, le radar arrive à estimer l’épaisseur de neige selon un écart-type entre 13 et 39 %. vii Finalement, j’ai équipé le radar d’un système de géolocalisation à haute précision. Ainsi équipé, le radar a une marge d’erreur de géolocalisation d’en moyenne <5 cm. À partir de la mesure radar, on peut déduire la distance entre le haut et le bas du manteau neigeux. En plus de l’épaisseur de neige, on obtient également des points de données qui permettent d’interpoler un modèle d’élévation de la surface solide sous-jacente. J’ai utilisé la méthode de structure triangulaire (TIN) pour toutes les interpolations. Le système offre beaucoup de flexibilité puisqu’il peut être installé sur un RPAS ou une motoneige. Ces outils épaulent la modélisation du couvert neigeux en fournissant des données sur un secteur, plutôt que sur un seul point. Les données peuvent servir à entraîner et à valider les modèles. Ainsi améliorés, ils peuvent, par exemple, permettre de prédire la taille, le niveau de santé et les déplacements de populations d’ongulés, dont la survie dépend de la qualité de la neige. (Langlois et coll., 2017.) Au même titre que la validation de modèles de neige, les outils présentés permettent de comparer et de valider d’autres données de télédétection (par ex. satellites) et d’élargir notre champ de compréhension. Finalement, les cartes ainsi créées peuvent aider les écologistes à évaluer l’état d’un écosystème en leur donnant accès à une plus grande quantité d’information sur le manteau neigeux qu’avec les coupes de neige traditionnelles.Abstract: Snow is highly variable in time and space and thus many observation points are needed to describe the present state of the snowpack accurately. This description of the state of the snowpack is necessary to validate and improve snow modeling efforts and remote sensing applications. The traditional snowpit analysis delivers a highly detailed picture of the present state of the snow in a particular location but lacks the distribution in space and time as it is a time-consuming method. On the opposite end of the spatial scale are orbital solutions covering the surface of the Earth in regular intervals, but at the cost of a much lower resolution. To improve the ability to collect spatial snow data efficiently during a field campaign, we developed a custom-made, remotely piloted aircraft system (RPAS) to deliver snow depth maps over a few hundred square meters by using Structure-from-Motion (SfM). The RPAS is capable of flying in extremely low temperatures where no commercial solutions are available. The system achieves a horizontal resolution of 6 cm with snow depth RMSE of 39% without vegetation (48.5% with vegetation) As the SfM method does not distinguish between different snow layers, I developed an algorithm for a frequency modulated continuous wave (FMCW) radar that distinguishes between the two main snow layers that are found regularly in the Arctic: “Depth Hoar” and “Wind Slab”. The distinction is important as these characteristics allow to determine the amount of water stored in the snow that will be available for the ecosystem during the melt season. Depending on site conditions, the radar estimates the snow depth with an RMSE between 13% and 39%. v Finally, I equipped the radar with a high precision geolocation system. With this setup, the geolocation uncertainty of the radar on average < 5 cm. From the radar measurement, the distance to the top and the bottom of the snowpack can be extracted. In addition to snow depth, it also delivers data points to interpolate an elevation model of the underlying solid surface. I used the Triangular Irregular Network (TIN) method for any interpolation. The system can be mounted on RPAS and snowmobiles and thus delivers a lot of flexibility. These tools will assist snow modeling as they provide data from an area instead of a single point. The data can be used to force or validate the models. Improved models will help to predict the size, health, and movements of ungulate populations, as their survival depends on it (Langlois et al., 2017). Similar to the validation of snow models, the presented tools allow a comparison and validation of other remote sensing data (e.g. satellite) and improve the understanding limitations. Finally, the resulting maps can be used by ecologist to better asses the state of the ecosystem as they have a more complete picture of the snow cover on a larger scale that it could be achieved with traditional snowpits

ALO4: Angle localization and orientation system with four receivers

This paper presents a 2D indoor localization and orientation system based on a TDOA (Time Difference of Arrival) technique. It uses an array of receivers (four low-cost ultrasonic resonant devices in a square distribution) to implement low-computational-effort DOA (Direction of Arrival) algorithms, based on assumed plane-wave reception. The system only demands two transmitters at well-known positions on the ceiling of the room for obtaining the node position and orientation when it is deployed on the floor of the room. This system has been tested using a Xilinx Spartan-3A FPGA that implements a 52 MHz MicroBlaze. The experimental results include a total of 1,440 points, obtaining a mean localization error of 5.17 cm and a mean orientation error of 3.34 degrees. For this system, the localization and orientation processes are executed in less than 50 us.This work has been supported by the Spanish Ministerio de Ciencia e Innovacion under project TEC2009-0987

ALO: An ultrasound system for localization and orientation based on angles

This is the author’s version of a work that was accepted for publication in Microelectronics Journal. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Microelectronics Journal, Vol 44, Issue 10, (October 2013). http://dx.doi.org/10.1016/j.mejo.2013.01.001This paper presents a low cost system based on ultrasound transducers to obtain the localization and orientation information of a mobile node, such as a robot, in a 2D indoor space. The system applies a new differential time of arrival (DTOA) technique with reduced computational cost, which is called ALO (angle localization and orientation). Instead of directly calculating its position, the system calculates the direction of arrival of the received ultrasonic signal and, through it, its position and orientation. A prototype of a robot has been built in order to show the validity of the method through experimental results

HALO4: Horizontal Angle Localization and Orientation System with 4 Receivers and Based on Ultrasounds

The final publication is available at Springer via http://dx.doi.org/10.1007/s10846-015-0283-2This paper presents a low cost ultrasonic localization and orientation system based on the DTOA (Differential Time Of Arrival) technique. The proposed system consists in deploying any number of autonomous nodes at the floor of a room and place some transmitters at the ceiling. Each node shall have four ultrasonic receivers to obtain the basic measures for the localization and orientation systems, and the coverage area of the system is defined by any region covered by at least three transmitters. The localization system is based on an estimation process of the horizontal angle of the node with respect to the transmitters. This implementation allows deploying the transmitters at different heights and ignores the error introduced by an incorrect estimation of the ultrasonic signal speed. The computational effort of the proposed system is greater than other ALO (Angle Localization and Orientation) systems, needing a minimization process to obtain the localization results, but it is smaller than in other typical techniques, like those based on the intersection of hyperboloids.This work has been supported by the Spanish Ministerio de Ciencia e Innovación under project TEC2009-09871

Advanced GNSS-R instruments for altimetric and scatterometric applications

This work is the result of more than eight years during a bachelor thesis, a master thesis, and the Ph.D. thesis dedicated to the development of the Microwave Interferometric Reflectometer (MIR) instrument. It summarizes all the knowledge acquired during this time, and describes the MIR instrument as detailed as possible. MIR is a Global Navigation Satellite System - Reflectometer (GNSS-R), that is, an instrument that uses Global Navigation Satellite System (GNSS) signals scattered on the Earth's surface to retrieve geophysical parameters. These signals are received below the noise level, but since they have been spread in the frequency domain using spread-spectrum techniques, and in particular using the so-called Pseudo Random Noise (PRN) codes, it is still possible to retrieve them because of the large correlation gain achieved. In GNSS-R, two main techniques are used for this purpose: the conventional technique cGNSS-R and the interferometric one iGNSS-R, each with its pros and cons. In the former technique, the reflected signal is cross-correlated against a locally generated clean-replica of the transmitted signal. In the latter technique the reflected signal is cross-correlated with the direct one. Nowadays multiple GNSS systems coexist, transmitting narrow and wide, open and private signals. A comparison between systems, signals, and techniques in fair conditions is necessary. The MIR instrument has been designed as an airborne instrument for that purpose: the instrument has two arrays, an up-looking one, and a down-looking one, each with 19 dual-band antennas in a hexagonal distribution. The instrument is able to form 2 beams at each frequency band (L1/E1, and L5/E5A), which are pointing continuously to the desired satellites taking into account their position, as well as the instrument's position and attitude. The data is sampled and stored for later post-processing. Last but not least, MIR is auto-calibrated using similar signals to the ones transmitted by the GNSS satellites. During the instrument development, the Distance Measurement Equipment/TACtical Air Navigation (DME/TACAN) signals from the Barcelona airport threatened to disrupt the interferometric technique. These signals were also studied, and it was concluded that the use of a mitigation systems were as strongly recommended. The interferometric technique was also affected by the unwanted contribution of other satellites. The impact of these contributions was studied using real data gathered during this Ph.D. thesis. During these 8 years, the instrument was designed, built, tested, and calibrated. A field campaign was carried out in Australia between May 2018 and June 2018 to determine the instrument's accuracy in sensing soil moisture and sea altimetry. This work describes each of these steps in detail and aims to be helpful for those who decide to continue the legacy of this instrument.Este trabajo es el resultado de más de 8 años de doctorado dedicados al desarrollo del instrumento Microwave Interferometric Reflectometer (MIR). Esta tesis resume todo el conocimiento adquirido durante este tiempo, y describe el MIR lo más detalladamente posible. El MIR es un Reflectómetro de señales de Sistemas Globales de Navegación por Satélite (GNSS-R), es decir, es un instrumento que usa señales de GNSS reflejadas en la superficie de la tierra para obtener parámetros geofísicos. Estas señales son recibidas bajo el nivel de ruido, pero dado que han sido ensanchadas en el dominio frecuencial usando técnicas de espectro ensanchado, y en particular usando códigos Pseudo Random Noise (PRN), es todavía posible recibirlas debido a la elevada ganancia de correlación. En GNSS-R existen dos técnicas para este propósito: la convencional (cGNSS-R), y la interferométrica (iGNSS-R), cada una con sus pros y sus contras. En la primera se calcula la correlación cruzada de la señal reflejada y de una réplica generada del código transmitido. En la segunda técnica se calcula la correlación cruzada de la señal reflejada y de la señal directa. Hoy en día muchos sistemas GNSS coexisten, transmitiendo señales de distintos anchos de banda, algunas públicas y otras privadas. Una comparación entre sistemas, señales, y técnicas en condiciones justas es necesaria. El MIR es un instrumento aerotransportado diseñado como para ese propósito: el instrumento tiene dos arrays de antenas, uno apuntando al cielo, y otro apuntando al suelo, cada uno con 19 antenas doble banda en una distribución hexagonal. El instrumento puede formar 2 haces en cada banda frecuencial (L1/E1 y L5/E5A) que apuntan continuamente a los satélites deseados teniendo en cuenta su posición, y la posición y actitud del instrumento. Los datos son guardados para ser procesados posteriormente. Por último pero no menos importante, el MIR se calibra usando señales similares a las transmitidas por los satélites de GNSS. Durante el desarrollo del instrumento, señales del sistema Distance Measuremt Equi Distance Measurement Equipment/TACtical Air Navigation (DME/TACAN) del aeropuerto de Barcelona mostraron ser una amenaza para la técnica interferométrica. Estas señales fueron estudiadas y se concluyó que era encarecidamente recomendado el uso de sistemas de mitigación de interferencias. La técnica interferométrica también se ve afectada por las contribuciones no deseadas de otros satélites, llamado cross-talk. El impacto del cross-talk fue estudiado usando datos reales tomados durante esta tesis doctoral. A lo largo de estos 8 años el instrumento ha sido diseñado, construido, testeado y calibrado. Una campaña de medidas fue llevada a cabo en Australia entre Mayo de 2018 y Junio de 2018 para determinar la capacidad del instrumento para estimar la humedad del terreno y la altura del mar. Este documento describe cada uno de estos pasos al detalle y espera resultar útil para aquellos que decidan continuar con el legado de este instrumento.Postprint (published version

Development of a drone-based miniaturized Flexible Microwave Payload (FMPL) for GNSS-Reflectometry and L-band radiometry

This project has been developed in collaboration with the NanosatLab UPC, which develops CubeSats for educational and scientific purposes and in-orbit technology demonstration. More specifically, the laboratory is focused on remote sensing systems. In recent years, the NanosatLab UPC has been developing the Flexible Microwave PayLoad (FMPL), the integration of different microwave remote sensing equipment in a single system: reflectometry Global Navigation Satellite System (GNSS) signals (GNSS-R) and microwave radiometry (MWR) in L-band. In 2022, the second version of this system, FMPL-2, is in orbit on board the CubeSat 3Cat5, which has provided precious scientific data on the climate of the earth and the evolution of climate change. The first version, FMPL-1, will be launched in the coming months aboard CubeSat 3Cat4. The third version, FMPL-3, is now ready for launch on board the CubeSat GNSSaS. From space, FMPL has proven to be a very useful tool for studying climate change. This work aims to design, build and test the first FMPL for drones, the FMPL-D. This new platform will be used to evaluate new versions of FMPL. It will also be a valuable tool to study the characteristics of soil, water, ice and vegetation locally and with a spatial resolution much greater than that which can be obtained from a satellite. The results presented in this thesis put the complexity of these systems into perspective. Firstly, in the results of the radiometer, an effect of distortion and destruction of the data obtained due to the radio frequency interference received during the measurement campaigns has been observed, highlighting the need for detection and mitigation systems interference for ground observation missions. For the GNSS reflectometry instrument, multiple flights were conducted in which large amounts of data were collected, the processing of which is still in progress. Preliminary results indicate good characteristics of the radio frequency chain. This Final Degree Project (TFG) is the first version of the FMPL-D, culminating in the system's first version and many lessons learned.Objectius de Desenvolupament Sostenible::13 - Acció per al Clim