With the increasing number of small Uncrewed Aerial Systems (sUAS) in the airspace, the need for robust Detect and Avoid (DAA) technologies is clear. This is especially true when considering the potential for non-cooperative aircraft with unknown intent. Many UAS use high resolution cameras to perform omnidirectional scans of their nearby airspace to localize traffic. These scans can be quite computationally expensive and often necessitate the use of costly and heavy hardware components. Ground-based solutions such as centralized, stationary towers are often expensive, difficult to proliferate, and have the disadvantage of not being onboard the aircraft and as such not always local to the airspace conflict. A feasibility exploration of acoustic detection and localization of non-cooperative aircraft using a low-cost microphone array, computationally inexpensive beamforming algorithms, and filtering techniques, is performed. The cost of the system is minimized by utilizing widely proliferated microphone hardware originally designed for short-range voice detection, as well as a small Uncrewed Aerial Systems (sUAS) from a developmental kit. Lastly, an exploration is conducted to maximize the detection range of the microphone system. A comparison of filtering techniques to try to filter sUAS self-noise is compared to alternative methods such as a ballistic sampling period where the motors of the sUAS are momentarily turned off to reduce noise. A final recommendation of a multi-sensor suite of microphones, cameras, along with other potential sensors, is determined. / Master of Science / As the number of drones increases throughout many industries, safe usage becomes very important. Industries such as search and rescue, infrastructure surveying, package delivery, and more, all have novel uses for drones that could change the way those industries operate. It is easy to imagine the benefit of same-day shipping with package-carrying drones, the quick location of a missing person by a search and rescue drone, and so on. However, obstacles such as buildings, trees, and other air traffic pose an obvious risk. Current methods to detect other aircraft often rely on cameras onboard the aircraft to spot nearby traffic. Other methods include using centralized stations on the ground to relay information about positioning between cooperating aircraft. These technologies provide functionality, but often can be expensive, heavy, require computers with large processing power, or assume the cooperation of the aircraft. An analysis of audio based detection of nearby drones is conducted. The microphones used were originally intended for use in home applications as a voice assistant. Programming techniques were used to listen and identify the sound of a nearby drone. Depending on the location of the drone, its sound would arrive to the microphones in unique time delays, providing a method of estimating the drone's position. Testing was performed on the ground and in the air to analyze the distance at which this microphone group could find a drone. Ultimately, a recommendation for the inclusion of microphones in a suite of sensors was made.

Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and UAS collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS photogrammetry to detect tornado damage. UAS imagery, combined with Structure from Motion (SfM) output, can directly be used to train models to detect tornado damage. In this research, we develop a CNN that can classify tornado damage in forests using SfM-derived orthophotos and digital surface models. The findings indicate that a CNN approach provides a higher accuracy than random forest classification, and that DSM-based derivatives add predictive value over the use of the orthophoto mosaic alone. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology. / Master of Science / Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and Unmanned Aerial System (UAS) collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS imagery to detect tornado damage. UAS imagery, combined with 3D models, can directly be used to train machine learning models to automatically detect tornado damage. In this research, we develop a machine learning model that can classify tornado damage in forests using UAS imagery and 3D derivatives. The findings indicate that the machine learning model approach provides a higher accuracy than traditional techniques. In addition, the 3D derivatives add value over the use of only the UAS imagery. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology.

Uncrewed aerial systems (UAS) have steadily become more prevalent in both defense and industrial applications. Nuclear detection and deterrence is one such field that has given rise to many new opportunities for UAS operations. There is a need to research and develop methods to integrate existing radiation detection technology with UAS capable of flying low-altitude missions. This low-altitude scanning can be achieved by combining small and lightweight radiation detectors and state-of-the-art aircraft and avionics. High resolution mapping can then be conducted using the results of these scans. Significant work has been conducted in this field by both private industry and academic institutions, including the Uncrewed Systems Lab (USL) at Virginia Tech. This work seeks to expand this body of knowledge and provide practical experimental information to showcase and validate the efficacy of radiation detection via UAS. Multiple missions were conducted using samples of 137Cs and 60Co as a radioactive source. Various filtering methods were applied to the results of these missions to produce visual maps that aid in the localization of an unknown source to compare various flight parameters. In addition, significant work was conducted to characterize two radiation detectors available to the USL to provide metrics to assist in the UAS design and flight planning. Finally, the detectors were taken to Savannah River National Laboratories to conduct experiments to provide information to aid future designs and missions that wish to detect a wider variety of radioactive sources. / Master of Science / Drones are becoming more common in many applications for both industry and defense. One of these applications is in the field of nuclear detection which seeks to both regulate the shipping of radioactive material as well as aid response to nuclear disasters. Methods need to be researched to combine existing radiation detectors with new drone technology. These new state-of-the-art drones are capable of flying at very low altitudes which can allow for the use of small and lightweight radiation detectors. Past work in this area, including at the Uncrewed Systems Lab (USL) at Virginia Tech, has explored larger scale aircraft as well as simulated radioactive sources. This work expands the existing knowledge of this field by providing scan results from real radioactive sources and drone flights. Multiple search flights were conducted using small quantities of radioactive cesium and cobalt. Maps were then produced using the information from these flights to showcase the system's ability to quickly locate the areas of high radioactivity. Flights were flown with different altitudes and speeds to determine the effects on mapping accuracy. Finally, experiments were conducted at Savannah River National Laboratories on a variety of more controlled nuclear materials to help inform future drone designs and mission planning.

Syftet med detta examensarbete har varit att konstruera ett kollisionsvarningsljus till en drönare åt Etteplan Industry AB. Företaget använder denna för att utföra tjänster åt olika företag så som flygfotografering och 3D-modelleringar.Svårigheterna i detta arbete har legat i bristen på plats och vikt samt att hålla effektförbrukningen så låg som möjligt för att inte påverka flygtiden för mycket. Samtidigt finns en hel del krav från luftfartsorgan på hur ett kollisionsvarningsljus ska konstrueras. Mycket fokus har legat på att testa och välja ut de LEDs med så mycket lumens/watt som möjligt utan att överskrida de riktlinjer som fanns angående effektförbrukning och vikt. För att åstadkomma detta har strömsnåla komponenter använts samtidigt som ett PCB har designats så litet som möjligt.Produkten har uppfyllt de krav som ställts, även om de resulterande strömmarna inte riktigt blev enligt förväntan på grund av olika faktorer. Produkten har ännu inte testats i luften på grund av att företagets UAS varit ute på uppdrag, men i slutändan blev ändå alla parter nöjda med resultatet.

To overcome some limitations of manual insect sampling in soybeans, an unmanned aerial vehicle (UAV) sampling platform was developed that could collect insects in a sweep net attached to the bottom of a UAV. Before this UAV sampling platform can be used to make management decisions, correlations with manual sweep net and/or drop cloth sampling methods are needed. This will allow action thresholds for the various pests to be calculated for the UAV sampling platform. To make the correlations, 87 soybean fields were sampled during 2020 and 2021 with each of 4 sampling methods, a UAV travelling 50-m, the same UAV travelling 25-m, 25 manual sweeps with sweep net, and a 1.5-row-m sample on a drop cloth. Data were compiled for 12 insect pests of soybeans in 5 families. Significant positive correlations between all sampling methods showed that all methods were useful for sampling all the insects of interest.

Small Unmanned Aerial Vehicles (SUAVs) are rapidly being adopted in the National Airspace (NAS) but experience a much higher failure rate than traditional aircraft. These SUAVs are quickly becoming complex enough to investigate alternative methods of failure analysis. This thesis proposes a method of expanding on the Fault Tree Analysis (FTA) method to a Bayesian Belief Network (BBN) model. FTA is demonstrated to be a special case of BBN and BBN can allow for more complex interactions between nodes than is allowed by FTA. A model can be investigated to determine the components to which failure is most sensitive and allow for redundancies or mitigations against those failures. The introduced method is then applied to the Virginia Tech ESPAARO SUAV. / Master of Science

With the rapid proliferation of small unmanned aircraft systems (UAS), the risk of mid-air collisions is growing, as is the risk associated with the malicious use of these systems. The airborne detect-and-avoid (ABDAA) problem and the counter-UAS problem have similar sensing requirements for detecting and tracking airborne threats. In this dissertation, two image-based sensing methods are merged to mimic human vision in support of counter-UAS applications. In the proposed sensing system architecture, a ``peripheral vision'' camera (with a fisheye lens) provides a large field-of-view while a ``central vision'' camera (with a perspective lens) provides high resolution imagery of a specific object. This pair form a heterogeneous stereo vision system that can support range resolution. A novel peripheral-central vision (PCV) system to detect, localize, and classify an airborne threat is first introduced. To improve the developed PCV system's capability, three novel algorithms for the PCV system are devised: a model-based path prediction algorithm for fixed-wing unmanned aircraft, a multiple threat scheduling algorithm considering not only the risk of threats but also the time required for observation, and the heterogeneous stereo-vision optimal placement (HSOP) algorithm providing optimal locations for multiple PCV systems to minimize the localization error of threat aircraft. The performance of algorithms is assessed using an experimental data set and simulations. / Doctor of Philosophy / With the rapid proliferation of small unmanned aircraft systems (UAS), the risk of mid-air collisions is growing, as is the risk associated with the malicious use of these systems. The sensing technologies for detecting and tracking airborne threats have been developed to solve these UAS-related problems. In this dissertation, two image-based sensing methods are merged to mimic human vision in support of counter-UAS applications. In the proposed sensing system architecture, a ``peripheral vision'' camera (with a fisheye lens) provides a large field-of-view while a ``central vision'' camera (with a perspective lens) provides high resolution imagery of a specific object. This pair enables estimation of an object location using the different viewpoints of the different cameras (denoted as ``heterogeneous stereo vision.'') A novel peripheral-central vision (PCV) system to detect an airborne threat, estimate the location of the threat, and determine the threat class (e.g. aircraft, bird) is first introduced. To improve the developed PCV system's capability, three novel algorithms for the PCV system are devised: an algorithm to predict the future path of an fixed-wing unmanned aircraft, an algorithm to decide an efficient observation schedule for multiple threats, and an algorithm that provides optimal locations for multiple PCV systems to estimate the threat position better. The performance of algorithms is assessed using an experimental data set and simulations.

As the cost of unmanned aerial systems (UAS) and their sensing payloads decrease the practical applications for such systems have begun expanding rapidly. Couple the decreased cost of UAS with the need for increased crop yields under minimal applications of agrochemicals, and the immense potential for UAS in commercial agriculture becomes immediately apparent. What the agriculture community needs is a cost effective method for the field-wide monitoring of crops in order to determine the precise application of fertilizers and pesticides to reduce their use and prevent environmental pollution. To that end, this thesis presents an unmanned aerial system aimed at monitoring a crop's status. The system presented uses a Yamaha RMAX unmanned helicopter, operated by Virginia Tech']s Unmanned Systems Lab (USL), as the base platform. Integrated with helicopter is a dual-band multispectral camera that simultaneously captures images in the visible and near-infrared (NIR) spectrums. The UAS is flown over a quarter acre corn crop undergoing a fertilizer rate study of two hybrids. Images gathered by the camera are post-processed to form a Normalized Difference Vegetative Index (NDVI) image. The NDVI images are used to detect the most nutrient deficient corn of the study with a 5% margin of error. Average NDVI calculated from the images correlates well to measured grain yield and accurately identifies when one hybrid reaches its yield plateau. A secondary test flight over a late-season tobacco field illustrates the system's capabilities to identify blocks of highly stressed crops. Finally, a method for segmenting bleached tobacco leaves from green leaves is presented, and the segmentation results are able to provide a reasonable estimation of the bleached tobacco content per image. / Master of Science

The purpose of the study was to compile a map of Furuvik theme park using UAS-photogrammetry and evaluate the products quality by performing a map control. The map control is carried out with guidelines from new and old HMK-documents and how such an evaluation is carried out in our neighbouring countries. At the same time, a time study was carried out on the project's workflow as a request from the University of Gävle (HiG) for a future Master's degree program in Land Surveying. The flight was carried out with a multicopter from Altigator. Prior to the flights, flight signals were placed and as well as, known points (stompunkter), were measured with SWEPOS network-RTK (real-time kinematic). The flight resulted in 1036 images, which in PhotoScan were joined together by block adjustment and generated an orthophotomosaic and a digital elevation model were generated. In ArcMap, from the orthomosaic, a map was produced, which was then controlled using measured control points. The results in the plan points show that the difference between objects in the produced map and their known coordinates varies radially between 0.0014 m and 0.029 m. The mean deviation is 0.009 m with the standard uncertainty (Sp) 0.014 m and the root mean square (RMS) 0,014 m. All requirements in HMK-Geodatakvalitet (Geodata Quality), HMK-Flygfotografering (Aerial Photography), HMK-Kartografi (Cartography), and similar documents from the Norwegian and Finnish national land survey were fulfilled. The requirements of the newer HMK documents on geodata quality and aerial photography are reasonable while HMK cartography needs updating as the requirements are too low, 0.07 m To control the height model, 18 control profiles were measured in according to the Swedish technical specification SIS-TS 21144: 2016. RMS in height for the entire area was 0.032 m. The duration of the study's implementation was documented to produce a time study that resulted in 374 hours of work during nine weeks. / Syftet med studien var att med hjälp av UAS-fotogrammetri framställa en karta över Furuviks nöjespark och utvärdera produktens kvalitet i form av en kartkontroll. Kartkontrollen genomfördes med riktlinjer från nya och gamla HMK-dokument samt hur en sådan utvärdering utförs i våra grannländer. Samtidigt utfördes en tidsstudie över projektets arbetsgång som ett önskemål från Högskolan i Gävle (HiG) för ett framtida civilingenjörsprogram inom lantmäteriteknik. Flygningen genomfördes med en multikopter från Altigator. Inför flygningarna placerades flygsignaler ut som liksom stompunkter mättes in med SWEPOS nätverks-RTK (real time kinematic). Flygningen resulterade i 1036 bilder som fogades samman i PhotoScan genom blockutjämning och genererade en ortotfotomosaik samt en markmodell. I ArcMap framställdes, ur ortofotomosaiken, en karta som sedan kontrollerades med hjälp av inmätta markpunkter i form av stickprov. Resultatet i plan av stickproven visar att skillnaden mellan objekt i den producerade kartan och motsvarande objekt inmätta i området varierar radiellt mellan 0,0014 m och 0,029 m. Medelavvikelsen radiellt är 0,014 m med standardosäkerheten (Sp) 0,014 m. Samtliga krav i HMK-Geodatakvalitet, HMK-Flygfotografering, HMK-Kartografi samt norska och finska styrdokument uppfylldes. Kraven i de nyare HMK-dokumenten om geodatakvalitet och flygfotografering har följt den tekniska utvecklingen medans HMK-Kartografi behöver uppdateras då kraven är för låga, 0,07 m. För att kontrollera markmodellen mättes 18 kontrollprofiler in i enlighet med den tekniska specifikationen SIS-TS 21144:2016. Standardosäkerheten i höjd för hela området resulterade i 0,032 m. Tidsåtgången för studiens genomförande dokumenterades för att framställa en tidsstudie som resulterade i 374 arbetstimmar under nio veckor.

Studiens syfte var att genom UAS-fotogrammetri se om det var möjligt att uppnå en mätosäkerhet på 2–3 cm samt se om det är möjligt att använda sprayfärgade kryss som markstöd istället för masonitplattor med målade timglas och ändå uppnå samma mätosäkerhet. Detta gjordes från två olika flyghöjder, 80 m och 110 m för att få en till dimension på studien. Markstöden mättes in med GNSS och i studien användes UAS DJI Phantom 4 v2.0 vid flygfotograferingen. I plan kontrollerades kartan genom detaljmätning med hjälp av multistation etablerad med 180-sekunders metoden. Kontroll av kartan i höjd gjordes genom inmätning av kontrollprofiler med GNSS och multistation. Totalt bearbetades data från 4 inmätningar, data från 80 m där markstöd bestått av masonitplattor respektive sprayfärgade kryss och det samma från 110 m. Databearbetningen utfördes i Agisoft PhotoScan där bilderna bearbetades till en ortofotomosaik, DEM och DSM. Ortofotomosaiken och DEM importerades sedan till ArcMap för skapande av baskarta och för kontroll av koordinaterna i plan. Markmodellen importerades till SBG Geo för vidare bearbetning och kontroll av avvikelse mellan kontrollprofilerna och DEM.  Resultatet av 42 st detaljmätningar gjordes genom beräkning av RMS-värdet mellan inmätta koordinater och motsvarande punkt i kartan. Vid flygfotografering från 80 m visade timglas ett RMS-värde på 0,038 m och kryss ett RMS-värde på 0,039 m. Motsvarande från 110 m visar att timglas gav ett RMS-värde på 0,062 m och kryss på 0,048 m. Alla inmätningar utom timglas från 110 m klarar toleransen mot HMK – Geodatakvalitet som är 5 cm och när enbart marknära objekt mättes gav det ett RMS-värde i plan på 0,026 m för timglas från 80 m och 0,023 m för kryss. 2–3 cm mätosäkerhet uppnåddes därmed. Från 110 m blev värdet 0,054 m med timglas och 0,035 m med kryss.  Kontroll av höjdosäkerhet gjordes enligt SIS-TS 21144:2016, där 12 kontrollprofiler mättes in och jämfördes mot DEM. Resultatet från 80 m med timglas som markstöd visade en total medelavvikelse på 0,006 m med 0,019 m i standardosäkerhet. Från samma flyghöjd, men med inmätningar av kryss visade ett resultat om -0,001 m med standardosäkerhet 0,030 m. Från den högre flyghöjden med timglas genererades en total medelavvikelse på 0,010 m med standardosäkerhet 0,033 m. Motsvarande genererade kryss en total medelavvikelse på 0,026 m med standardosäkerhet 0,040. Alla 4 markmodellerna klarar den efterfrågade mätosäkerheten om 2–3 cm. / The aim of this study was to collect data through UAS photogrammetry and investigate if it was possible to achieve an uncertainty of 2-3 cm. The second aim was to investigate if it was possible to use spray-colored crosses as control points (GCP) instead of hourglass-painted fibreboards to achieve the same uncertainty. This was done from two different flight heights, 80 m and 110 m to add another dimension to the investigation. The GCPs were measured with GNSS and in the study a UAS DJI Phantom 4 v2.0 was used for aerial photography. The plane coordinates was checked by measuring details using multistation established with the 180-second method. Height control was done by measuring profiles with GNSS and multistation. All together data from 4 measurements were processed; from 80 m where GCPs consisted of hourglass and crosses, respectively, and the same from 110 m. The processing was performed in Agisoft PhotoScan where the images were aligned to an orthophoto mosaic. A DEM and DSM were also created. The orthophoto mosaic and DEM were used in ArcMap for digitizing a base map and for checking the plane coordinates. The DEM was imported to SBG Geo for further processing and control of deviation between profiles and DEM.  The result of the 42 measured details was made by calculating the RMSE value between the measured plane coordinates and the corresponding points in the map. In aerial photography from 80 m, hourglass showed an RMSE value of 0.038 m and crosses an RMSE value of 0.039 m. Corresponding from 110 m, hourglass gave an RMSE value of 0.062 m and a cross of 0.048 m. All measurements except hourglass from 110 m can withstand the tolerance to HMK – Geodatakvalitet (2017) which is 5 cm. If only ground-level objects were to be measured the RMSE value of 0.026 m for hourglass from 80 m and 0.023 m for crosses reached the wanted measurement uncertainties of 2–3 cm. From 110 m the value was 0.054 m with hourglass and 0.035 m with cross.  The control of the height uncertainty was made in accordance with SIS-TS 21144:2016, where 12 profiles were measured and compared with the DEM. The result from 80 m with hourglass showed a total mean deviation (MD) of 0.006 m with 0.019 m in standard deviation (SD). From the same flight height, but with crosses, a result of -0.001 m with SD showed 0.030 m. From the higher height with hourglass, a total MD of 0.010 m with SD 0.033 m was generated. The corresponding crosses got a MD of 0,026 m and a SD of 0,040 m. All 4 DEM can handle the required measurement uncertainty of 2-3 cm.