In recent years, the technical progress of Unmanned Aerial Vehicles (UAV) has increased rapidly. This has made it economically possible for tasks where one earlier needed helicopters or airplanes. By equipping UAVs with high-quality cameras, the utility has expanded. With detailed photos from a UAV together with digital photogrammetry, special software can be used to create point clouds, 3D-models, elevation models and ortomosaics over smaller geographic areas.  Due to the easy access and that the use of UAVs has increased among private users, the Swedish transport agency has set up rules that determine how UAVs should be used. In these regulations, UAVs are separated into different groups from A1 to A3 based on the weight of the aircraft. The regulations make it illegal to fly a UAV heavier than 250 g over people who are not informed about the flight. Since there are several industries where it could be interesting to fly over people, DJI has manufactured a UAV-model called DJI Mini 2. This UAV weighs 249 g and can therefore be flown over people. In this project, two different flights have been carried out. One with the DJI Mini 2 weighing 249 g and one with the DJI Mavic 2 Pro weighing just under 1 kg. During the flight, overlapping pictures were captured and orthomosaic and elevation models were created from those. The purpose of the project was to investigate if the same accuracy can be achieved with a mini-UAV as with a larger UAV. The study also investigated whether the models were accurate enough to use as a ground model along roads and railroads.  In the Dronelink software, two different flight routs were created over Örsholmens IP in the east­ern part of Karlstad, Sweden. Dronelink created the flightpath based on the UAVs different specifications and the overlap between images that were acquired. A geodetic control network was created using GNSS-technology. Five points were measured twice with 45 minutes in between and calculated in SBG GEO. The next day, a surveying total station was established centrally between the points in the geodetic control network. Thereafter 19 ground control points were measured, and 398 ground points distributed in 20 different control profiles. After the points were measured, the flights were carried out. A total of two flights by each UAV model were made the same day. Then the flight with the best results were used for further processing.  The photos taken were processed in Agisoft Metashape. In the software, point clouds, elevation models and orthomosaic were created. The elevation model was then compared to the measured control profiles. The orthomosaic was used to compare the ground control points.  The results showed that the elevation model created with Mavic 2 Pro was within the SIS tolerance on all different types of surfaces to be used for modelling along roads and railways. The elevation model from the DJI Mini 2 was withing the tolerance on grass and asphalt but not on gravel sur­face. The mean deviation on gravel was -1,37 cm outside the tolerance. The planar comparison showed that both models were withing the tolerance to achieve HMK level 3 standard. / Den tekniska utvecklingen av obemannade flygfarkoster har de senaste åren utvecklats i snabb takt. Flygfarkosterna som ofta benämns UAV (Unmanned Aerial Vehicle) har gjort det möjligt att till en låg kostnad genomföra uppdrag som tidigare krävt helikopter eller flygplan. Genom att utrusta en UAV med en högkvalitativ kamera så ökar dess användningsområde. Med hjälp av detaljerade digi­tala bilder tagna med UAV, och digital fotogrammetri i specialprogramvaror kan punktmoln, 3D-modeller, höjdmodeller och ortomosaik över mindre geografiska områden framställas.  På grund av att UAV-användningen ökat snabbt så har Transportstyrelsen tagit fram ett regelverk för hur en UAV får användas. I detta regelverk så delas olika UAV-modeller in i grupper från A1 till A3 baserat på vikten. Regelverket gör att en UAV som är tyngre än 250 g inte får flygas över männi­skor som inte är informerade om att flygningen pågår.  Eftersom det i flera branscher kan vara intressant att flyga över platser där människor rör sig fritt så har DJI tillverkat en UAV-modell som heter DJI Mini 2. Denna UAV väger 249 g och får därför flygas över människor. I detta projekt har två olika flygningar gjorts med DJI Mini 2 som väger 249 g och med Mavic 2 Pro som väger knappt 1 kg. Under flygningen togs överlappande bilder och höjdmodeller och ortomosaik skapades från dessa. Syftet med arbetet är att undersöka om lika hög noggrannhet kan uppnås med en mini-UAV som med en större modell. Studien undersökte även om modellerna uppnår kraven i SIS-TS 21144:2016 och HMK för att få användas till att skapa markmodell längs väg och järnväg.  I programvaran Dronelink så skapades två flygrutter över Örsholmens IP i den östra delen av Karl­stad. Dronelink skapade rutterna baserat på de olika UAV-specifikationerna och bildöverlappet som valdes för flygningarna. På platsen skapades ett nytt stomnät med hjälp av GNSS-teknik. Fem punk­ter mättes in två gånger med 45 minuters mellanrum och beräknades i SBG GEO. Dagen efter eta­blerades en totalstation centralt mellan punkterna mot stomnätet. Därefter mättes 19 markstöd in och 398 punkter fördelat i 20 olika kontrollprofiler. Efter att punkterna var inmätta genomfördes flygningarna. Totalt gjordes två flygningar med de olika UAV-modellerna på samma dag. Därefter valdes de flygningar som visade bäst resultat för vidare bearbetning.  Bilderna som togs bearbetades i programvaran Agisoft Metashape och punktmoln, höjdmodell och ortomosaik genererades. Höjdmodellen användes sedan för att jämföras mot de inmätta kontrollprofilerna. Ortomosaiken jämfördes med de inmätta kontrollpunkterna.   Resultatet visade att höjdmodellen som skapades med Mavic 2 Pro var inom SIS-TS 21144:2016 toleranserna på samtliga underlag för att få användas för modellskapande på väg och järnväg. Höjdmodellen som skapades med DJI Mini 2 var inom toleransen på gräs och asfalt men inte på grusunderlag. Medelavvikelsen på grus var -1,37 cm utanför toleransen. Vid jämförelsen i plan vi­sade resultatet att båda modellerna var inom HMKs toleranser för att uppnå HMK-standardnivå 3.

Digitala terrängmodeller (DTM:er) är ett vanligt förekommande verktyg i planering av olika samhällsutvecklande projekt inom stat, kommun och den privata sektorn. Inom planering för byggnationer av väg och järnväg används ofta SIS-TS 21144:2016 som ett dokument för styrning av produktionsprocessen vid framtagning av DTM:er, eller markmodeller. Med anledning av att ny teknik öppnar för möjligheter till snabbare, effektivare och klimatsmartare insamling av data, har denna studie till syfte att utvärdera Unmanned Aerial System (UAS) med Light Detection and Ranging (LiDAR) från YellowScan och dess mätosäkerhet i höjd. I denna undersökning jämförs resultatet från studien med klass 2 i SIS-TS 21144:2016 för flyghöjderna 50 m och 80 m samt för skanningsvinklarna 0 (lod), 10, 20 och 40 grader. Platsen för studien är belägen strax sydväst om Gävle i en nedlagd grustäkt med både hårt packat och något lösare underlag. Storleken för studieområdet begränsades till 200 x 300 m, vilket ger en 6 ha stor yta. Med utrustning för mätning med GNSS (Global Navigation Satellite System) mättes två stompunkter in med Nätverks-RTK (Real Time Kinematic). Därefter skapades ett stomnät med åtta punkter. Totalt mättes 26 kontrollytor in för jämförelser mot insamlade LiDAR-data. Datainsamlingen utfördes med obemannad flygfarkost (Unmanned Aerial Vehicle, UAV), GeoDrone X4L, utrustad med LiDAR-skannern YellowScan Surveyor meden integrerad IMU (Inertial Measurement Unit) från Applanix. Tillsammans bildade dessa enheter ett obemannat flygsystem (UAS) som kunde fjärrstyras och kommunicera sin position. All bearbetning utfördes i programvara från Terrasolid, baserat på data från flygrutten som först bearbetats i YellowScan CloudStation. Punkterna klassificerades för att urskilja marklassade punkter som användes vid generering av DTM:er. En justering av punktmolnet gjordes med avsikt att höja marklassade punkter för att motverka det brus som förekommer i data. Kontrollytorna kunde nu jämföras mot DTM:en och analyseras. Resultaten i studien visar att YellowScan Surveyor uppnår ett Root Mean Square (RMS) i höjd på 0,024 m vid 50 meters flyghöjd, vilket innebär 0,047 m utvidgad mätosäkerhet (2-sigma, 95 %). Även vid 80 meter uppnås relativt låg mätosäkerhet i höjd med ett RMS på 0,040 m. Resultaten i studien visar också att påverkan av mätning i en större skanningsvinkel inte är den enda faktor som försämrar resultatet. / Digital terrain models (DTMs) are a commonly used tool in planning various development projects within the state, municipalities, and the private sector. In planning for road and rail construction, the Swedish technical specification SIS-TS21144:2016 is often used as a document for controlling the production process of DTMs. Given that new technology opens the possibilities for faster, more efficient, and climate-smart data collection, this study aims to evaluate Unmanned Aerial System (UAS) with Light Detection and Ranging (LiDAR) from YellowScan and to evaluate the measurement uncertainty in height. In this study, the results of the study are compared with class 2 SIS-TS 21144:2016 for the flight heights 50 m and 80 m and the scanning angles 0 (in nadir), 10, 20 and 40 degrees. The site of the study is located just southwest of Gävle in a closed gravel pit with both hard packed and slightly looser substrates. The size of the study area was limited to 200 x 300 m, equivalent to 6 hectares. With Global Navigation Satellite System (GNSS) equipment, two control points were measured with Network-RTK (Real Time Kinematic). Subsequently, a control network of eight points was created. A total of 26 control grids were measured for comparisons of collected LiDAR data. The data collection was carried out with the Unmanned Aerial Vehicle (UAV) GeoDrone X4L equipped with LiDAR Scanner YellowScan Surveyor with an integrated Inertial Measurement Unit (IMU) from Applanix. Together, these units formed an UAS that could be remotely controlled and communicate its position. All processing was performed in software from Terrasolid, based on data from the flight route that was first processed in YellowScan CloudStation. The points were classified to distinguish ground points used in the generation of the DTM. An adjustment of the point cloud was made with the intention of raising ground level points to reduce the noise present in the data. The control grids could then be compared to the DTM and analysed. The results of the study show that YellowScan Surveyor achieves a Root Mean Square (RMS) in height of 0,024 m at 50 meters flight altitude, which equals 0,047m expanded measurement uncertainty (2 sigma level, 95 %). Even at 80 meters, relatively low uncertainty is achieved with an RMS of 0,040 m. The results of the study indicate that the influence of measurements at a wider scanning angle is not the only factor that deteriorates the results.

I samband med en trafikolycka är det ofta viktigt att återställa platsen till det normala så snabbt som möjligt. Emellanåt måste olycksplatsen dokumenteras för att orsaken till olyckan ska kunna utredas i ett senare skede. Traditionellt har detta arbete utförts genom att fotografera platsen och mäta olika avstånd. På senare tid har även terrester laserskanning kommit att bli ett tillförlitligt alternativ. Med det sagt är det tänkbart att även fotogrammetri och andra typer av laserskanning skulle kunna användas för att uppnå liknande resultat.  Syftet med denna studie är att utforska hur handhållen laserskanning och UAS-fotogrammetri kan användas för att dokumentera en trafikolycka. Detta uppnås genom att utvärdera punktmolnens lägesosäkerhet och visuella kvalitet. Vidare utforskas fördelar och nackdelar med respektive metod, bland annat sett till tidsåtgång och kostnader, för att slutligen komma fram till vilken metod som lämpar sig bäst för att dokumentera en trafikolycka.  En trafikolycka med två inblandade bilar iscensattes och laserskannades till en början med den handhållna laserskannern Leica BLK2GO. Därefter samlades bilder in med den obemannade flygfarkosten Leica Aibot följt av att ett referenspunktmoln skapades med den terrestra laserskannern Leica C10. Genom att jämföra koordinater för kontrollpunkter i referenspunktmolnet med koordinaterna för motsvarande kontrollpunkter i de två andra punktmolnen kunde deras lägesosäkerheter bestämmas. Studiens resultat visar att både punktmolnet som framställdes med handhållen laserskanning och UAS-fotogrammetri har en lägesosäkerhet (standardosäkerhet) i 3D på 0,019 m. Båda metoderna är tillämpliga för att dokumentera en trafikolycka, men jämfört med terrester laserskanning är punktmolnen dock bristfälliga på olika sätt. BLK2GO producerar ett förhållandevis mörkt punktmoln och mörka objekt avbildas sämre än ljusare föremål. I punktmolnet som framställdes med Leica Aibot förekom påtagliga håligheter i bilarnas karosser. Handhållen laserskanning är en tidseffektiv metod medan UAS-fotogrammetri kan utföras till en lägre kostnad. Sammanfattningsvis går det inte att dra någon entydig slutsats om vilken metod som lämpar sig bäst för att dokumentera en trafikolycka. Valet beror på vilka omständigheter som råder på olycksplatsen. / In the event of a traffic accident, it is often important to restore the site to its normal condition as fast as possible. Occasionally, the accident scene must be documented so that the cause of the accident can be investigated at a later stage. Traditionally, this work has been performed by taking pictures of the site and measuring different distances. Lately, terrestrial laser scanning has also become a reliable alternative. With that said, it is possible that photogrammetry and other types of laser scanning also could be utilized to achieve similar results.    The aim of this study is to investigate how handheld laser scanning and UAS photogrammetry can be used to document a traffic accident. This is achieved by examining the positional uncertainty and visual quality of the point clouds. Moreover, the advantages and disadvantages of each method are explored, for instance in terms of time consumption and costs, in order to finally come to a conclusion of which method is best suited for documenting a traffic accident. A traffic accident with two involved cars was staged and initially laser scanned with the handheld laser scanner Leica BLK2GO. Thereafter, pictures were collected with the unmanned aerial vehicle Leica Aibot followed by the creation of a reference point cloud with the terrestrial laser scanner Leica C10. By comparing the coordinates of control points in the reference point cloud with the coordinates of the corresponding control points in the two other point clouds, their positional uncertainty could be determined. The results of the study show that both the point cloud produced by the handheld laser scanner and UAS photogrammetry have a positional uncertainty (standard uncertainty) of 0.019 m. Both methods are applicable for documenting a traffic accident but compared to terrestrial laser scanning, the point clouds are deficient in different ways. BLK2GO produces a relatively dark point cloud and dark objects are reproduced worse than lighter objects. In the point cloud produced by Leica Aibot, there were noticeable cavities in the bodies of the cars. Handheld laser scanning is a time-efficient method while UAS photogrammetry can be performed at a lower cost. In conclusion, it is not possible to arrive at an unambiguous conclusion with regards to which method that is best suited for documenting a traffic accident. The choice depends on the prevailing circumstances at the accident scene.

<p dir="ltr">Unmanned Aerial Systems (UAS) have the potential to drastically change how civil infrastructure is inspected, monitored, and managed. This innovative technology can ensure the inspector’s safety, provide additional inspection information, and reduce costs. However, a challenge arose as this industry expanded: a lack of standardized guidelines or minimum performance requirements to perform these operations. With no standard tests to verify UAS’ ability to conduct inspections and unknown detection capabilities, agencies are left to rely upon consultants’ or vendors’ promotional material and claims when considering UAS deployment. The following work proposes a series of performance-based assessments and procedural documentation to establish minimum standards for using UAS in bridge inspection applications. Through this work, the following performance-based tests have been developed: (1) a controlled environment simulating bridge geometries to assess the overall capability of a UAS used for bridge inspection [evaluation chamber], (2) an assessment of UAS performance under multiple environmental temperatures [environmental temperature chamber], (3) a UAS performance assessment under varying wind speeds [wind chamber], (4) a consolidated checklist compiling Federal Aviation Administration guidelines and best practices [flight checklist], (5) a field assessment of UAS under conditions analogous to on-site bridge inspection [practical test]. For infrastructure owners, embracing these performance-based assessments will help ensure that UAS meets a minimum level of performance and allow owners to verify and distinguish between various UAS used for bridge inspection. This work also discusses positive feedback from beta testing provided by industry and infrastructure owner representatives, showcasing the effectiveness of providing an authentic assessment of UAS bridge inspection capabilities. Future work encourages the wide implementation of this assessment program and encourages owners to refrain from using untested technology in the inspection of their infrastructure.</p>

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Aerocross Systems, Inc. is developing a low-cost unmanned airborne telemetry relay system to augment the USAF Air Armament Center’s Eglin Gulf Range instrumentation resources. The system is designed to remotely autotrack and relay S-Band telemetry and VHF/UHF voice communications from test articles beyond the line-of-sight of land-based instrumentation. The system consists of a medium altitude/endurance Unmanned Aerial Vehicle (UAV), a Mission Control Station, and a remotely operated telemetry/voice tracking and relay instrumentation suite. Successfully developed and deployed, the system will contribute to lower range costs while enhancing range instrumentation performance.

På senare år har tjuvjakten på noshörningar resulterat i ett kritiskt lågt bestånd. Detta examensarbete är en del av ett initiativ för att stoppa denna utveckling. Målet är att använda en UAV, utrustad med GPS och attitydsensorer, samt en värmekamera placerad på en gimbal, till att övervaka vilda djur. Genom att använda en värmekamera kan djuren lätt detekteras eftersom de antas vara varmare än sin omgivning. En modell av marken vid testområdet har använts för att möjliggöra positionering av detekterade djur, samt analys av vilka områden på marken som ses av kameran. Termen övervakning inkluderar detektion av djur, målföljning och planering av rutt för UAV:n. UAV:n ska kunna söka av ett område efter djur. För att göra detta krävs planering av trajektoria för UAV:n samt hur gimbalen ska förflyttas. Flera metoder för detta har utvärderats. UAV:n ska även kunna målfölja djur som har detekterats. Till detta har ett partikelfilter använts. För att associera mätningar till spår har Nearest Neighbor-metoden använts. Djuren detekteras genom att bildbehandla på videoströmmen som ges från värmekameran. För bildbehandlingen har flertalet metoder testats. Dessutom presenteras en omfattande beskrivning av hur en UAV fungerar och är uppbyggd. I denna beskrivs även nödvändiga delar för ett UAV-system. På grund av begränsningar i budgeten har ingen UAV inköpts. Istället har tester utförts från en gondol i Kolmården. Gondolen åker runt i testområdet med en konstant hastighet. Djur kunde lätt detekteras och målföljas givet en kall bakgrund. Då solen värmer upp marken är det svårare att särskilja djuren från marken och fler feldetektioner görs av bildbehandlingen / In recent years, the poaching of rhinoceros has decreased its numbers to critical levels. This thesis project is a part of an initiative to stop this development. The aim of this master thesis project is to use a UAV equipped with positioning and attitude sensors as well as a thermal camera, placed onto a gimbal, to perform wildlife surveillance. By using a thermal camera, the animals are easily detected as they are assumed to be warmer than the background. The term wildlife surveillance includes detection of animals, tracking, and planning of the UAV. The UAV should be able to search an area for animals, for this planning of the UAV trajectory and gimbal attitude is needed. Several approaches for this have been tested, both online and offline planning. The UAV should also be able to track the animals that are detected, for this a particle filter has been used. Here a problem of associating measurements to tracks arises. This has been solved by using the Nearest Neighbor algorithm together with gating. The animals are detected by performing image processing on the images received from the thermal camera. Multiple approaches have been evaluated. Furthermore, a thoroughly worked description of how a UAV is working as well as how it is built up is presented. Here also necessary parts to make up a full unmanned aerial system are described. This chapter can be seen as a good guide for beginners, to the UAV field, interested in knowing how a UAV works and the most common parts of such a system. A ground model of Kolmården, where the testing has been conducted, has been used in this thesis. The use of this enables positioning of the detected animals and checking if an area is occluded for the camera. Unfortunately, due to budget limitations, no UAV was purchased. Instead, testing has been conducted from a gondola in Kolmården traveling across the test area with a constant speed. To use the gondola as the platform, for the sensors and the thermal camera, is essentially the same as using a UAV as both alternatives are located in the air above the animals, both are traveling around the map and both are stable for good weather conditions. The animals could easily be detected and tracked given a cold background. When the sun heats up the ground, it is harder to distinguish the animals in the thermal video, and more false detections in the image processing appear.

he digital revolution in the latter part of the twentieth century has resulted in the increased use and development of Cyber-Physical Systems. Two of which are Unmanned Aerial Systems (UAS) and Integrated Vehicle Health Management (IVHM). Both are relatively new areas of interest to academia, military, and commercial organisations. Designing IVHM for a UAS is no easy task – the complexity inherent in UAS, with projects involving multiple partners/organisations; multiple stakeholders are also interested in the IVHM. IVHM needs to justify itself throughout the life of the UAS, and the lack of established knowledge makes it hard to know where to start. The establishment and analysis of requirements for IVHM on UAS is known to be important and costly – and for IVHM a complex one. There are multiple stakeholders to satisfy and ultimately the needs of the customer, all demanding different things from the IVHM, and with limited resources they need to be prioritised. There are also many hindrances to this: differences in language between stakeholders, customers failing to see the benefits, scheduling conflicts, no operational data. The contribution to knowledge in this thesis is the IVHM Requirements Deployment (IVHM-RD) – a method for a designer of UAS IVHM to build a tool which can consolidate and evaluate the various stakeholder’s requirements. When the tool is subsequently populated with knowledge from individual Subject Matter Experts (SMEs), it provides a prioritised set of IVHM requirements. The IVHM-RD has been tested on two design cases and generalised for the use with other designs. Analysis of the process has been conducted and in addition the results of the design cases have been analysed in three ways: how the results relate to each design case, comparison between the two cases, and how much the relationships between requirements are understood. A validation exercise has also been conducted to establish the legitimacy of the IVHM-RD process. This research is likely to have an impact on the elicitation and analysis of IVHM requirements for UAS – and the wider design process of IVHM. The IVHM-RD process should also prove of use to designers of IVHM on other assets. The populations of the design cases also provide information which could be useful to other designer and future research.

This dissertation investigates vulnerabilities in unmanned vehicles and how to successfully detect and counteract them. As we entrust unmanned vehicles with more responsibilities (e.g. fire-fighting, search and rescue, package delivery), it is crucial to ensure their safe operation. These systems often have not been designed to protect against an intelligent attacker or considering all possible interactions between the physical dynamics and the internal logic. Robust control strategies can verify that the system behaves normally under bounded disturbances, and formal verification methods can check that the system logic operates normally under ideal conditions. However, critical vulnerabilities exist in the intersection of these fields that are addressed in this work. Due to the complex nature of this interaction, only trivial examples have previously been pursued. This work focuses on efficient real-time methods for verification and validation of unmanned vehicles under disturbances and cyberattacks. The efficiency of the verification and validation algorithm is necessary to run it onboard an unmanned vehicle, where it can be used for self diagnosis. We begin with simple linear systems and step to more complex examples with non-linearities. During this progression, new methods are developed to cope with the challenges introduced. We also address how to counter the threat of unmanned aerial systems (UASs) under hostile control by developing and testing an estimation and control scheme for an air-to-air counter UAS system.<br>

abstract: Drylands (arid and semi-arid grassland ecosystems) cover about 40% of the Earth's surface and support over 40% of the human population, most of which is in emerging economies. Human development of drylands leads to topsoil loss, and over the last 160 years, woody plants have encroached on drylands, both of which have implications for maintaining soil viability. Understanding the spatial variability in erosion and soil organic carbon and total nitrogen under varying geomorphic and biotic forcing in drylands is therefore of paramount importance. This study focuses on how two plants, palo verde (Parkinsonia microphylla, nitrogen-fixing) and jojoba (Simmondsia chinensis, non-nitrogen fixing), affect sediment transport and soil organic carbon and total nitrogen pools in a dryland environment north of Phoenix, Arizona. Bulk samples were systematically collected from the top 10 cm of soil in twelve catenae to control for the existence and type of plants, location to canopy (sub- or intercanopy, up- or downslope), aspect, and distance from the divide. Samples were measured for soil organic carbon and total nitrogen and an unmanned aerial system-derived digital elevation map of the field site was created for spatial analysis. A subset of the samples was measured for the short-lived isotopes 137Cs and 210Pbex, which serve as proxy erosion rates. Erosional soils were found to have less organic carbon and total nitrogen than depositional soils. There were clear differences in the data between the two plant types: jojoba catenae had higher short-lived isotope activity, lower carbon and nitrogen, and smaller canopies than those of palo verde, suggesting lower erosion rates and nutrient contributions from jojoba plants. This research quantifies the importance of biota on influencing hillslope and soil dynamics in a semi-arid field site in central AZ and finishes with a discussion on the global implications for soil sustainability. / Dissertation/Thesis / Masters Thesis Geological Sciences 2018

As small unmanned aerial systems (UAS) are introduced into the national airspace, measures must be introduced to ensure that they do not interfere with manned aviation and other UAS. Radar provides an attractive solution because of its inherent range accuracy and because it works in diverse weather and lighting conditions. Traditional radar systems, however, are large and high power and do not meet the size, weight and power (SWaP) constraints imposed by UAS, and fully integrated automotive solution do not provide the necessary range. This thesis proposes a compact radar system that meets both the SWaP and range requirements for UAS and can act as a standalone sensor for a sense and avoid system (SAA). The system meets the field of view requirements motivated by the UAS sensing problem (120deg x 30deg) and tracks targets in range and azimuthal angle using a four element phased array receiver. The phased array receiver implements real time correlation and beamforming using a field programmable gate array (FPGA) and can track multiple targets simultaneously. Excluding antennas, the radar transceiver and signal processing platform weighs approximately 120g and is approximately the size of a whiteboard eraser (2.25in x 4in x 1in), which meets the payload requirements of many small (<25kg) UAS. To our knowledge, this is the first real time phased array radar that meets the sensing and SWaP requirements for small UAS.Our testing was done with the radar system on the ground, aimed at airborne UAS targets. Using antennas with a gain of 12 dB, and 800 milliwatts of transmitted power, the system detects UAS targets with a radar cross section of less than 0.1 square meters up to 150 meters away. The ground based system demonstrates radar detectability of extremely small UAS targets, and is scalable to further ranges by increasing antenna gain or adding additional elements. Based on our success in detecting airborne UAS, we conclude that radar remains a feasible option for a UAS collision avoidance sensor.