Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Fault detection and isolation (FDI) is an important aspect of effective fault tolerant control architectures. The Electronic System Laboratory at Stellenbosch University identified the need to study viable methods of FDI. In this research two FDI methods for actuator failures on the Meraka Modular UAV are investigated. The Meraka Modular UAV is an unmanned aircraft that was developed by the CSIR. A simple six degree of freedom non-linear mathematical model is developed that presents a platform on which the two FDI methods are formulated. The theoretical model is used in a simulation environment to extensively test and compare the performance of the proposed FDI methods in different types of flight conditions. The first method investigated is a multiple model adaptive estimator (MMAE), which incorporates a bank of Kalman filters. Each Kalman filter in the MMAE is conditioned for each expected actuator fault scenario. The limitations of using linear Kalman filters are explained and they are replaced by extended Kalman filters, whose associated advantages and disadvantages are discussed. Each filter in the bank of Kalman filters produces a residual vector and residual covariance matrix. This information is subjected to a Bayes classifier to determine the fault scenario which will have the highest likelihood of being active. The second method that is studied incorporates the parity space approach for FDI. The parity space consists of the parity relations that quantify all the analytical redundancies available between the sensors’ outputs and actuator inputs of a system. A transformation matrix is then optimised to transform these parity relations into residuals that are specially sensitive to specific actuator faults. Actuator faults cause the parity space residuals’ variance to increase. A cumulative summation procedure is used to determine when the residuals’ variance has changed sufficiently to indicate an actuator fault. A pseudoinverse actuator estimation scheme is used to extract the actuator deflections from the parity relations. The FDI performance is tested by deliberately failing specific actuators of the Meraka Modular UAV in-flight. The flight test data is then used to analyse and compare the performance of the two FDI methods investigated in the research. It is found that, for the specific Meraka Modular UAV, the FDI performs as expected with disturbance effects and actuator excitation influencing the FDI effectiveness. The research shows that the bank of Kalman filters creates less false alarms whereas the parity space FDI is more sensitive to faults. It is illustrated that FDI can be improved with active actuator excitation and process noise estimation techniques, delivering promising results. / AFRIKAANSE OPSOMMING: Fout-deteksie en -isolasie (FDI) is belangrik vir ’n stelsel se beheerder om foute te kan hanteer. Die Elektroniese Stelsellabaratorium (ESL) by die Universiteit van Stellenbosch het die behoefte geïdentifiseer om te gaan kyk na moontlike FDI-stelsels wat gebruik kan word op hul onbemande vliegtuie (OV). In hierdie navorsing is daar na twee FDI-metodes gekyk wat op die Meraka Modulêre OV toegepas kan word. Die Meraka Modulêre OV is ’n vliegtuig wat deur die WNNR ontwikkel is. ’n Eenvoudige sesgrade- van-vryheid, nie-liniêre wiskundige model van die Meraka Modulêre OV is ontwikkel, en die FDI-metodes is rondom hierdie model geformuleer. Die teoretiese model is gebruik in ’n simulasie-omgewing en die werkverrigting van die twee FDI-metodes is in verskillende vlug-omstandighede getoets en vergelyk. Die eerste metode waarna gekyk is, was ’n multi-model aanpasbare afskatter (MMAA), wat ’n bank van Kalman-filters gebruik. Elke Kalman-filter in die MMAA is gekondisioneer vir elke denkbare aktueerder-fout. Die beperkinge rondom liniêre Kalman-filters is uitgelig en vergelyk met uitgebreide Kalman-filters, waarvan die voor- en nadele bespreek is. Elke filter in die MMAA produseer ’n residu-vektor en residu-kovariansiematriks. Hierdie informasie is na ’n Bayes-klassifiseerder gestuur om te bepaal watter fout-senario die grootste waarskynlikheid het om aktief te wees. Die tweede metode waarna gekyk is, het die pariteitsruimte vir FDI gebruik. Die pariteitsruimte is uit al die pariteitsverwantskappe opgebou wat die verhoudings tussen al die insette en uitsette van ’n sisteem kwantifiseer. ’n Transformasie-matriks is geoptimaliseer om hierdie pariteitsverwantskappe te transformeer na residue wat elkeen sensitief is tot ’n spesikiefe aktueerderfout. ’n Spesifieke aktueerderfout veroorsaak dat ’n spesifieke residu se variansie verhoog. ’n Kummulatiewe sommeringsproses is dan gebruik om te bepaal of die variansie genoegsaam toegeneem het. Sodoende kon daar bepaal word of ’n fout ontstaan het. ’n Pseudo-inversaktueerder-afskattingstegniek is gebruik om die afgeskatte aktueerderdefleksie uit die pariteitsverwantskappe te onttrek. Die FDI-werkverrigtinge van die twee metodes is getoets deur sekere aktueerders met opset te laat faal gedurende vlugtoetse. Die vlugtoetsdata is gebruik om die werkverrigting van die FDI-metodes te analiseer en met mekaar te vergelyk. Met die spesifieke Meraka Modulêre OV is, soos te wagte, bevind dat versteurings en aktueerderopwekking ’n groot invloed op die FDI’s se werkverrigtinge toon.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: In order to navigate dynamic, cluttered environments safely, fully autonomous Unmanned Aerial Vehicles (UAVs) are required to plan conflict-free trajectories between two states in position-time space efficiently and reliably. Kinodynamic planning for vehicles with non-holonomic dynamic constraints is an NP-hard problem which is usually addressed using sampling-based, probabilistically complete motion planning algorithms. These algorithms are often applied in conjunction with a finite set of simple geometric motion primitives which encapsulate the dynamic constraints of the vehicle. This ensures that composite trajectories generated by the planning algorithm adhere to the vehicle dynamics. For many vehicles, accurate tracking of position-based trajectories is a non-trivial problem which demands complicated control techniques with high energy requirements. In an effort to reduce control complexity and thus also energy consumption, a generic Local Planning Method (LPM), able to plan trajectories based on implicitly-defined motion primitives, is developed in this project. This allows the planning algorithm to construct trajectories which are based on simulated results of vehicle motion under the control of a rudimentary auto-pilot, as opposed to a more complicated position-tracking system. The LPM abstracts motion primitives in such a way that it may theoretically be made applicable to various vehicles and control systems through simple substitution of the motion primitive set. The LPM, which is based on a variation of the Levenberg-Marquardt Algorithm (LMA), is integrated into a well-known Probabilistic Roadmap (PRM) kinodynamic planning algorithm which is known to work well in dynamic and cluttered environments. The complete motion planning algorithm is tested thoroughly in various simulated environments, using a vehicle model and controllers which have been previously verified against a real UAV during practical flight tests. / AFRIKAANSE OPSOMMING: Ten einde dinamiese, voorwerpryke omgewings veilig te navigeer, word daar vereis dat volledig-outonome onbemande lugvoertuie konflikvrye trajekte tussen twee posisie-tydtoestande doeltreffend en betroubaar kan beplan. Kinodinamiese beplanning is ’n NPmoeilike probleem wat gewoonlik deur middel van probabilisties-volledige beplanningsalgoritmes aangespreek word . Hierdie algoritmes word dikwels in kombinasie met ’n eindige stel eenvoudige geometriese maneuvers, wat die dinamiese beperkings van die voertuig omvat, ingespan. Sodanig word daar verseker dat trajekte wat deur die beplaningsalgoritme saamgestel is aan die dinamiese beperkings van die voertuig voldoen. Vir baie voertuie, is die akkurate volging van posisie-gebaseerde trajekte ’n nie-triviale probleem wat die gebruik van ingewikkelde, energie-intensiewe beheertegnieke vereis. In ’n poging om beheer-kompleksiteit, en dus energie-verbruik, te verminder, word ’n generiese plaaslike-beplanner voorgestel. Hierdie algoritme stel die groter kinodinamiese beplanner in staat daartoe om trajekte saam te stel wat op empiriese waarnemings van voertuig-trajekte gebaseer is. ’n Eenvoudige beheerstelsel kan dus gebruik word, in teenstelling met die meer ingewikkelde padvolgingsbeheerders wat benodig word om eenvoudige geometriese trajekte akkuraat te volg. Die plaaslike-beplanner abstraeer maneuvers in so ’n mate dat dit teoreties op verskeie voertuie en beheerstelsels van toepassing gemaak kan word deur eenvoudig die maneuver-stel te vervang. Die plaaslike-beplanner, wat afgelei is van die Levenberg-Marquardt-Algoritme (LMA), word in ’n welbekende “Probabilistic Roadmap” (PRM) kinodinamiese-beplanningsalgoritme geïntegreer. Dit word algemeen aanvaar dat die PRM effektief werk in dinamiese, voorwerpryke omgewings. Die volledige beplanningsalgoritme word deeglik in verskeie, gesimuleerde omgewings getoets op ’n voertuig-model en -beheerders wat voorheen vir akkuraatheid teenoor ’n werklike voertuig gekontroleer is tydens praktiese vlugtoetse.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: In this thesis the strategy for performing System Identification on an aircraft is presented. The ultimate aim of this document is to outline the steps required for successful aircraft parameter estimation within a Fault Tolerant Control Framework. A brief derivation of the classical 6 degree-of-freedom aircraft model is firstly presented. The derivation gives insight into the aircraft dynamics that are to be used to estimate the aircraft parameters, and provides a basis for the methods provided in this thesis. Different techniques of System Identification were evaluated, resulting in the choice of the Regression method to be used. This method, based on the Least-Squares method, is chosen because of its simplicity of use and because it does not require as much computational time as the other methods presented. Regression methods, including a recursive algorithm, are then applied to aircraft parameter estimation and practical considerations such as Identifiability and corrupted measurements are highlighted. The determination of unknown measurements required for System Identification of aircraft parameters is then discussed. Methods for both estimating and measuring the Angle-of-Attack (AoA) and angular accelerations are presented. The design and calibration of an AoA probe for AoA measurements, as well as a novel method that uses distributed sensors to determine the angular accelerations is also presented. The techniques presented in this thesis are then tested on a non-linear aircraft model. Through simulation it was shown that for the given sensor setup, the methods do not provide sufficiently accurate parameter estimates. When using the Regression method, obtaining measurements of the angle-of-attack solely through estimation causes problems in the estimation of the aerodynamic lift coefficients. Flight tests were performed and the data was analyzed. Similar issues as experienced with estimation done on the non-linear aircraft simulation, was found. Recommendations with regards to how to conduct future flight tests for system identification is proposed and possible sources of errors are highlighted. / AFRIKAANSE OPSOMMING: In hierdie tesis word die strategie vir die uitvoering van Stelsel Identifikasie op 'n vliegtuig aangebied. Die uiteindelike doel van hierdie document is om die stappe wat nodig is vir 'n suksesvolle vliegtuig parameter beraming, binne 'n Fout Tolerante Beheer Raamwerk, uit eente sit. 'n Kort afleiding van die klassieke 6 graad-van-vryheid vliegtuig model word eerstens aangebied. Die afleiding gee insig in die vliegtuig dinamika wat gebruik moet word om die vliegtuig parameters te beraam, en bied 'n basis vir die metodes wat in hierdie tesis verskyn. Verskillende tegnieke van Stelsel Identifikasie is geëvalueer, wat lei tot gebruik van die regressie-metode. Hierdie metode is gekies as gevolg van sy eenvoudigheid en omdat dit nie soveel berekening tyd as die ander metodes vereis nie. Regressie metodes, insluitend 'n rekursiewe algoritme, word dan toegepas op vliegtuig parameter beraming en praktiese orwegings soos identifiseerbaarheid en korrupte metings word uitgelig. Die bepaling van onbekende afmetings wat benodig is, word vir Stelsel Identifisering van die vliegtuig parameters bespreek. Metodes om die invalshoek en hoekige versnellings te meet en beraam, word aangebied. Die ontwerp en kalibrasie van 'n invalshoek sensor vir invalshoek metings, sowel as 'n nuwe metode wat gebruik maak van verspreide sensore om die hoekversnellings te bepaal, word ook aangebied. Die tegnieke wat in hierdie tesis aangebied is, word dan op 'n nie-lineêre vliegtuig model getoets. Deur simulasie is dit getoon dat die metodes vir die gegewe sensor opstelling nie voldoende akkurate parameters beraam nie. Dit is ook bewys dat met die gebruik van die Regressie metode, die vekryging van metings van die invalshoek slegs deur skatting, probleme in die beraming van die aerodinamiese lug koëffisiente veroorsaak. Die tegnieke wat in hierdie tesis verskyn, word dan op werklike vlug data toegepas.Vlugtoetse is uitgevoer en die data is ontleed. Aanbeveling met betrekking tot hoe om toekomstige vlug toetse vir Stelsel Identifikasiete word voorgestel, en moontlike bronne van foute word uitgelig.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: This project concerns motion planning for a rotary wing UAV, where vehicle controllers are already in place, and map data is readily available to a collision detection module. In broad terms, the goal of the motion planning algorithm is to provide a safe (i.e. obstacle free) flight path between an initial- and goal waypoint. This project looks at two specific motion planning algorithms, the Rapidly Exploring Random Tree (or RRT*), and the Probabilistic Roadmap Method (or PRM). The primary focus of this project is learning how these algorithms behave in specific environments and an in depth analysis is done on their differences. A secondary focus is the execution of planned paths via a Simulink simulation and lastly, this project also looks at the effect of path replanning. The work done in this project enables a rotary wing UAV to autonomously navigate an uncertain, dynamic and cluttered environment. The work also provides insight into the choice of an algorithm for a given environment: knowing which algorithm performs better can save valuable processing time and will make the entire system more responsive. / AFRIKAANSE OPSOMMING: ’n Tipiese vliegstuuroutomaat is daartoe in staat om ’n onbemande lugvaartvoertuig (UAV) so te stuur dat ’n stel gedefinieerde punte gevolg word. Die punte moet egter vooraf beplan word, en indien enige verandering nodig is (bv. as gevolg van veranderinge in die omgewing) is dit nodig dat ’n menslike operateur betrokke moet raak. Vir voertuie om ten volle outonoom te kan navigeer, moet die voertuig in staat wees om te kan reageer op veranderende situasies. Vir hierdie doel word kinodinamiese beplanningsalgoritmes en konflikdeteksiemetodes gebruik. Hierdie projek behels kinodinamiese beplanningsalgoritmes vir ’n onbemande helikopter, waar die beheerders vir die voertuig reeds in plek is, en omgewingsdata beskikbaar is vir ’n konflikdeteksie-module. In breë terme is die doel van die kinodinamiese beplanningsalgoritme om ’n veilige (d.w.s ’n konflikvrye) vlugpad tussen ’n begin- en eindpunt te vind. Hierdie projek kyk na twee spesifieke kinodinamiese beplanningsalgoritmes, die “Rapidly exploring Random Tree*” (of RRT*), en die “Probabilistic Roadmap Method” (of PRM). Die primêre fokus van hierdie projek is om die gedrag van hierdie algoritmes in spesifieke omgewings te analiseer en ’n volledige analise te doen op hul verskille. ’n Sekondêre fokus is die uitvoering van ’n beplande vlugpad d.m.v ’n Simulink-simulasie, en laastens kyk hierdie projek ook na die effek van padherbeplanning. Die werk wat gedoen is in hierdie projek stel ’n onbemande helikopter in staat om outonoom te navigeer in ’n onsekere, dinamiese en besige omgewing. Die werk bied ook insig in die keuse van ’n algoritme vir ’n gegewe omgewing: om te weet watter algoritme beter uitvoertye het kan waardevolle verwerkingstyd bespaar, en verseker dat die hele stelsel vinniger kan reageer.

Thesis (MScEng)-- Stellenbosch University, 2013. / ENGLISH ABSTRACT: This thesis presents the development of a flight control system (FCS) for an unmanned, unpowered parafoil and the integration with an existing parafoil system in collaboration with a team at the University of Cape Town (UCT). The main goal of the FCS is to autonomously guide the parafoil from an arbitrary deployment position to a desired landing target. A nonlinear 8 degrees of freedom (8-DOF) parafoil model by C. Redelinghuys is incorporated into a MATLAB Simulink simulation environment. The non-linear model is numerically linearised and modal decomposition techniques are used to analyse the natural modes of motion. All modes are determined to be stable but a poorly damped lateral payload relative twist mode is present which causes large payload yaw oscillations. The FCS is divided into stability augmentation, control and guidance subcomponents. Stability augmentation is proposed in the form of a yaw rate damper which provides artificial damping for the oscillatory payload twist mode. For control, a yaw rate controller is designed with the aim of a fast response while not exciting the payload twist oscillation. Subsequently, an existing guidance method is implemented for path following. Autonomous path planning and mission control logic is created, including an energy management (EM) method to eliminate excess height and a terminal guidance (TG) phase. The TG phase is the final turn before landing and is the last chance to influence landing accuracy. A TG algorithm is implemented which generates an optimal final turn and can be replanned en route to compensate for unknown wind and other disturbances. The FCS is implemented on existing avionics, integrated with the parafoil system and verified with hardware in the loop (HIL) simulations. Flight tests are presented but are limited to remote control (RC) tests that verify the integration of the avionics and the parafoil system and test preliminary FCS components. / AFRIKAANSE OPSOMMING: Hierdie tesis dra die ontwikkeling voor van ‘n vlug-beheerstelsel (VBS) vir ’n onbemande, onaangedrewe valskerm-sweeftuig (parafoil), asook die integrasie daarvan met ’n bestaande stelsel. Die projek is in samewerking met ’n span van die Universiteit van Kaapstad (UCT) uitgevoer. Die VBS se hoof doel is om die sweeftuig outonoom vanaf ’n arbitrêre beginpunt na ’n gewensde landingsteiken te lei. ’n Nie-lineêre 8 grade van vryheid sweeftuig model deur C. Redelinghuys is in die MATLAB Simulink omgewing geïnkorporeer. Die nie-lineêre model is numeries gelineariseer om ’n lineêre model te verkry, waarna die natuurlike gedrag van die tuig geanaliseer is. ’n Swak gedempte laterale draai ossillasie van die loonvrag is geïdentifiseer. Die VBS is opgedeel in stabiliteitstoevoeging, beheer en leiding. ’n Giertempo-demper (yaw rate damper) is as stabiliteitstoevoeging om die loonvrag ossillasie kunsmatig te demp, voorgestel. ’n Giertempo-beheerder is ontwerp met die klem op ’n vinnige reaksie terwyl die opwekking van die loonvrag ossillasie terselfdetyd verhoed word. Daarna is ’n bestaande metode vir trajekvolging geïmplementeer. Outonome padbeplanning en oorhoofse vlugplan logika is ontwikkel, insluitend ’n energie-bestuur (EB) metode, om van oortollige hoogte ontslae te raak, asook ’n terminale leiding (TL) metode. Die TL fase verwys na die finale draai voor landing en is die laaste kans om die landingsakkuraatheid te beïnvloed. ’n Bestaande TL algoritme is geïmplementeer wat ’n optimale trajek genereer en in staat is om vir wind en ander versteurings te kompenseer deur die trajek deurgaans te herbeplan. Die VBS is op bestaande avionika geïmplementeer, met die sweeftuigstelsel geïntegreer en met behulp van hardeware in die lus (HIL) simulasies geverifieer. Vlugtoetse is voorgedra, maar is egter beperk tot radio beheer vlugte wat die korrekte integrasie van die avionika en die voertuig toets, asook ’n beperkte aantal voormalige VBS toetse.

Dissertation (MTech (Mechanical Engineering))--Cape Peninsula University of Technology, 2011 / This paper presents the development of a data collection system for a small unmanned Aerial Vehicle (UAV) flight. The following three facets comprise of a UAV system: (1) a UAV aircraft; (2) onboard avionics; and (3) a ground control station subsystem (Taha et al., 2010:1). In this project, the UAV aircraft is based on the low-cost autonomous quad-rotator system named “Arducopter Quad”, where the onboard avionic system utilizes both an ArduPilot Mega (APM) on-board controller and IMU sensor shield, while the “Mission Planner” software operates as GCS software to gather essential flight data (Xiang & Tian, 2011:176). The approach provides the UAV system structure and both hardware and software with a small UAV data collection system, which is examined throughout the study. And introduce the concept of Arducopter dynamics for better understanding with its flight control. The study also considers the communication process between the UAV and the ground control station. The radio wave is an important aspect in the UAV data collection system (Austin, 2010:143). The literature review introduced the basis of the radio wave in respect of its travelling speed, and its characteristics of propagation, including how different frequencies will affect radio wave propagation. The aim of this project was to develop a platform for a small UAV real-time data collection system. The pendulum system was involved to simulate the “Roll” movement of the small UAV, while real-time IMU sensor data was successfully collected at ground control station (GCS), both serial communication and wireless communication, which was applied in the data collection process. The microwave generator interference test proves that the 2.4 GHz XBee module is capable of establishing reliable indoor communication between the APM controller and the GCS. The work of this project is towards development of additional health monitoring technology to prevent the safety issue of the small UAV. The data collection system can be used as basis for the future research of real-time health monitoring for various small UAVs.

The Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific information on the current state of the coastal wetland habitats. In order to produce such assessments, digital datasets of the appropriate accuracy and scale are needed. These datasets include orthoimagery to delineate wetland areas and map vegetation cover as well as accurate 3-dimensional (3-D) models to characterize hydrology, physiochemistry, and habitat vulnerability. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The Boeing Joined-Wing SenorCraft is a novel aircraft design that has many potential benefits, especially for surveillance missions. However, computational studies have shown the potential for nonlinear structural responses in the joined-wing configuration due to aerodynamic loading that could result in aft wing buckling. The design, construction, and flight testing of a 1/9th scale, aeroelastically tuned model of the Joined-Wing SensorCraft has been the subject of an ongoing international collaboration aimed at experimentally demonstrating the nonlinear aeroelastic response in flight. To accurately measure and capture the configuration\'s potential for structural nonlinearity, the test article must exhibit equivalent structural flexibility and be designed to meet airworthiness standards. Previous work has demonstrated airworthiness through the successful flight of a Geometrically Scaled Remotely Piloted Vehicle. The work presented in this thesis involves evaluation of an aeroelastically tuned design through ground-based experimentation. The result of these experimental investigations has led to the conclusion that a full redesign of the forward and aft wings must be completed to demonstrate sufficient geometric nonlinearity for the follow-on Aeorelastically Tuned Remotely Piloted Vehicle. This thesis also presents flight test plans for the aeroelastically tuned RPV. / Master of Science

Unmanned aerial vehicles (UAV's) have recently gained popularity in military, civil service, agriculture, commercial, and hobby use. This is due in part to their affordability, which comes from advances in component technology. That technology includes microelectromechanical systems (MEMS) for inertial sensing, microprocessor technology for sequential algorithm processing, field programmable gate arrays (FPGA's) for parallel data processing, camera technology, global navigation satellite systems (GNSS's) for navigation, and battery technology such as the high energy density of lithium polymer batteries. Despite the success of the technology to date, there remains development before UAV's should be flying alongside manned aircraft or over populated areas. One concern is that UAV electronics are not as safe, reliable or robust as manned-aircraft electronics because UAV's are not certified by the FAA. Another concern for UAV operation is with control algorithms and sensors, particularly in the estimation of the aircraft state, which is the position, velocity, and orientation of the aircraft. Some problems, such as numerical stability of a control algorithm or flight in windy and turbulent conditions have only been solved for certain conditions of wind, weather, or maneuvers. Outside those conditions, the actual orientation of a flying craft can mislead to the control system, and the control system may not be able to recover without a crash. When pilots fly manned aircraft in instrument meteorological conditions, or conditions of limited visibility of the ground, terrain, and obstacles, the pilot must fly in a manner which avoids abrupt maneuvers which could disturb accuracy of the aircraft's instruments. In a UAV without a pilot, there is a need to estimate the position and orientation of a UAV in an absolute manner unambiguous relative to the Earth. The position and orientation estimate must not depend on carefully controlled flight paths, but instead the estimate must be robust in the presence of UAV flight dynamics. This thesis describes the design, implementation, and evaluation of a hardware platform for GPS based orientation sensing research. In this work, we considered a receiver with three or four RF sections, each connected to an antenna in a triangular or tetrahedral pyramid constellation. Specific requirements for the receiver hardware and functionality were created. Circuitry was designed to meet the requirements using commercial off-the-shelf (COTS) radio frequency (RF) modules, a mid-sized microcontroller, an FPGA, and other supporting components. A printed circuit board (PCB) was designed, fabricated, assembled, and tested. A GPS baseband processor was designed and coded in Verilog hardware description language. The design was synthesized and loaded to the FPGA, and the microcontroller was programmed to track satellites. With the hardware platform implemented, live satellite signals were found and tracked, and experiments were performed to explore the validity of GPS based orientation sensing using short antenna baselines. The platform successfully allows the user to develop correlator designs and explore carrier phase based orientation measurement using only software/Verilog modifications. Initial results of carrier phase based orientation sensing are promising, but the presence of multipath signal interference shows room for improvement to the baseband processing code.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.