General aircraft dynamic stability theory was used to predict the natural frequencies, damping ratios and time constants of the dynamic modes for three specific small UAVs with wingspans on the scale from 0.6 meters to 1.2 meters. Using USAF DatCom methods, a spreadsheet program for predicting the dynamic stability and handling qualities of small UAVs was created for use in the design stage of new small UAV concept development. This program was verified by inputting data for a Cessna-182, and by then comparing the program output with that of a similar program developed by DAR Corporation. Predictions with acceptable errors were made for all of the dynamic modes except for the spiral mode. The design tool was also used to verify and develop dynamic stability and handling qualities design guidelines for small UAV designers. Using this design tool, it was observed that small UAVs tend to exhibit higher natural frequencies of oscillation for all of the dynamic modes. Comparing the program outputs with military handling qualities specifications, the small UAVs at standard configurations fell outside the range of acceptable handling qualities for short-period mode natural frequency, even though multiple test pilots rated the flying qualities as acceptable. Using dynamic scaling methods to adjust the current military standards for the short period mode, a new scale was proposed specifically for small UAVs. This scale was verified by conducting flight tests of three small UAVs at various configurations until poor handling qualities were observed. These transitions were observed to occur at approximately the boundary predicted by the new, adjusted scale.

The potential of Unmanned Aerial Vehicles (UAVs) for surveillance and military applications is significant — with continued technical advances in the field. The number of incidents where UAVs have intruded into unauthorized areas has increased in recent years and armed drones are commonly used in modern warfare. It is therefore of great interest to investigate methods for UAVs to locate and track intruder drones to prevent and counter surveillance of unauthorized areas and attacks from intruder UAVs. This master’s thesis studied how two autonomous seeker UAVs can be used cooperatively to track and pursue a target UAV. To locate the target UAV, simulated measurements from received Radio Frequency (RF) signals were used by extracting bearing and Received Signal Strength (RSS) data. To track the target and predict its future position, the study employed an Extended Kalman Filter (EKF) on each seeker UAV, which acted together as a Mobile Wireless Sensor Network (MWSN). The thesis explored two formation control methods to keep the seeker UAVs in formation while pursuing the target drone. The formation methods used the predicted position of the target to produce reference positions and/or reference distances for a controller to follow. A Distributed Model Predictive Controller (DMPC) was implemented on the seeker UAVs to pursue the target while maintaining formation and avoiding collisions. The EKF, MPC, and formation methods were first evaluated individually in simulation to assess their performance and for parameter tuning. The respective modules were then combined into the complete system and tuned to achieve improved pursuit and formation in simulation. The results showed that, with the chosen parameters and with a high level of measurement noise, the seeker UAVs were able to pursue the target with a combined average distance error of less than 2 m when the target drone flew in a square pattern with a velocity of 2 m/s. The quality of the pursuit was highly affected by the increase in velocity of the target and the initial positions of the seekers, where a high velocity and a large initial deviation from the reference positions/distances resulted in poorer quality.

<p dir="ltr">This research explored a solution to a high accident rate in remotely operating Unmanned Aerial Vehicles (UAVs) in a complex environment; it presented a new Brain-Computer Interface (BCI) enabled supervisory control system to fuse human and machine intelligence seamlessly. This study was highly motivated by the critical need to enhance the safety and reliability of UAV operations, where accidents often stemmed from human errors during manual controls. Existing BCIs confronted the challenge of trading off a fully remote control by humans and an automated control by computers. This study met such a challenge with the proposed supervisory control system to optimize human-machine collaboration, prioritizing safety, adaptability, and precision in operation.</p><p dir="ltr">The research work included designing, training, and testing BCI and the BCI-enabled control system. It was customized to control a UAV where the user’s motion intents and cognitive states were monitored to implement hybrid human and machine controls. The DJI Tello drone was used as an intelligent machine to illustrate the application of the proposed control system and evaluate its effectiveness through two case studies. The first case study was designed to train a subject and assess the confidence level for BCI in capturing and classifying the subject’s motion intents. The second case study illustrated the application of BCI in controlling the drone to fulfill its missions.</p><p dir="ltr">The proposed supervisory control system was at the forefront of cognitive state monitoring to leverage the power of an ML model. This model was innovative compared to conventional methods in that it could capture complicated patterns within raw EEG data and make decisions to adopt an ensemble learning strategy with the XGBoost. One of the key innovations was capturing the user’s intents and interpreting these into control commands using the EmotivBCI app. Despite the headset's predefined set of detectable features, the system could train the user’s mind to generate control commands for all six degrees of freedom of adapting to the quadcopter by creatively combining and extending mental commands, particularly in the context of the Yaw rotation. This strategic manipulation of commands showcased the system's flexibility in accommodating the intricate control requirements of an automated machine.</p><p dir="ltr">Another innovation of the proposed system was its real-time adaptability. The supervisory control system continuously monitors the user's cognitive state, allowing instantaneous adjustments in response to changing conditions. This innovation ensured that the control system was responsive to the user’s intent and adept at prioritizing safety through the arbitrating mechanism when necessary.</p>

Uninterrupted network connectivity is vital for real-time and mission-critical communication networks. The failure of Base Stations due to unforeseen circumstances such as natural disasters or emergencies can affect the coverage and capacity provided by terrestrial communication networks. The use of Unmanned Aerial Vehicles (UAVs) or drones in cellular networks is an upcoming area of research interest in 5G where the public sector and the communication service providers are fervently discussing it. The drones can be rapidly deployed to bridge the gaps in coverage or capacity of the network due to unforeseen circumstances. This thesis explores drone base stations' use for a simple hexagonal cell deployment scenario where the deployable base stations replace two failed macro base stations to improve the mean network capacity. Simulations show that the introduction of the deployable base stations indeed helps improve mean network capacity in case of one or multiple macro base station fail. The Genetic Algorithm is used to achieve Pareto optimality between downlink and uplink capacity of the simulated network. The simulation results show that introducing deployable nodes in a network can improve the network's capacity while also giving near-optimal transmit power values. / Oavbruten nätverksanslutning är avgörande för realtids- och missionskritiska kommunikationsnätverk. Fel på basstationer på grund av oförutsedda omständigheter som naturkatastrofer eller nödsituationer kan påverka täckningen och kapaciteten som tillhandahålls av markbundna kommunikationsnätverk. Användningen av Unmanned Aerial Vehicles (UAV) eller drönare i cellulära nätverk är ett kommande område av forskningsintresse inom 5G där den offentliga sektorn och leverantörerna av kommunikationstjänster ivrigt diskuterar det. Drönarna kan snabbt sättas in för att överbrygga klyftorna i nätverkets täckning eller kapacitet på grund av oförutsedda omständigheter. Denna avhandling utforskar drönarbasstationers användning för ett enkelt scenarie för hexagonal celldistribution där de utplacerbara basstationerna ersätter två misslyckade makrobasstationer för att förbättra den genomsnittliga nätverkskapaciteten. Simuleringar visar att introduktionen av de utplacerbara basstationerna verkligen hjälper till att förbättra den genomsnittliga nätverkskapaciteten i händelse av att en eller flera makrobasstationer misslyckas. Den genetiska algoritmen används för att uppnå Pareto-optimalitet mellan nedlänks- och upplänkkapaciteten i det simulerade nätverket. Simuleringsresultaten visar att införandet av utplacerbara noder i ett nätverk kan förbättra nätverkets kapacitet samtidigt som det ger nästan optimala värden för sändningseffekt.

Integrating unmanned aerial vehicles (UAVs) as aerial user equipments (UEs) into cellular networks is now considered as a promising solution to provide extensive wireless connectivity for supporting UAV-centric commercial or civilian applications. However, the co-existence of UAVs with conventional terrestrial UEs is one of the primary challenges for this solution. Flying at higher altitudes with maneuverability advantage, UAVs are able to establish line-of-sight (LoS) connectivity with more base stations (BSs) than terrestrial UEs. Although LoS connectivity reduces the communication delay of UAVs, they also simultaneously increase the interference that UAVs cause to terrestrial UEs. In scenarios involving multiple UAVs, LoS connectivity can even lead to interference issues among themselves. In addition, LoS connectivity leads to extensive overlapping coverage areas of multiple BSs for UAVs, forcing them to perform frequent handovers during the flight if the received signal strength (RSS)-based handover policy is employed. The trajectories and BS associations of UAVs, along with their radio resource allocation are essential design parameters aimed at enabling their seamless integration into cellular networks, with a particular focus on managing interference levels they generate and reducing the redundant handovers they performe. Hence, this thesis designs two joint trajectory and handover management approaches for single-UAV and multi-UAVs scenarios, respectively, aiming to minimize the weighted sum of three key performance indicators (KPIs): transmission delay, up-link interference, and handover numbers. The approaches are based on deep reinforcement learning (DRL) frameworks with dueling double deep Q-network (D3QN) and Q-learning with a MIXer network (QMIX) algorithms being selected as the training agents, respectively. The choice of these DRL algorithms is motivated by their capability in designing sequential decision-making policies consisting of trajectory design and handover management. Results show that the proposed approaches effectively address the aforementioned challenges while ensuring the low transmission delay of cellular-connected UAVs. These results are in contrast to the performance of benchmark scheme, which directs UAVs to follow the shortest path and perform handovers based on RSS. Specifically, when considering the single-UAV scenario, the D3QN-based approach reduces the up-link interference by 18% and the handover numbers by 90% with a 59% increase in transmission delay as compared to the benchmark. The equivalent delay increase is 15 microseconds, which is considered negligible. For the multi-UAVs scenario, the QMIX-based approach jointly optimizes three performance metrics as compared to the benchmark scheme, resulting in a 70% decrease in interference, a 91% decrease in handover numbers, and a 47% reduction in transmission delay. It is noteworthy that an increase of UAVs operating within the same network leads to performance degradation due to UAVs competing for communication resources and mutual interference. When transitioning from the single-UAV scenario to the multi-UAVs scenario, the performance of the benchmark scheme experiences a significant decline, with an increase of 199% in interference, 89% in handover numbers, and 652% in transmission delay. In contrast, the proposed QMIX algorithm effectively coordinates multiple UAVs, mitigating performance degradation and achieving performance similar to the D3QN algorithm applying in the single-UAV scenario: an interference increase of 9%, a handover numbers increase of 9% and a delay increase of 152%. The delay increase is attributed to the reduced communication resources available to each individual UAVs, given the constant communication resources of the network. / Att integrera obemannade flygfordon (UAV) som flyganvändarutrustning (UE) i cellulära nätverk anses nu vara en lovande lösning för att tillhandahålla omfattande trådlös anslutning för att stödja UAV-centrerade kommersiella eller civila tillämpningar. Men samexistensen av UAV med konventionella markbundna UE är en av de främsta utmaningarna för denna lösning. Flygande på högre höjder med manövrerbarhetsfördelar kan UAV:er etablera siktlinje (LoS)-anslutning med fler basstationer (BS) än markbundna UE. Även om LoS-anslutning minskar kommunikationsfördröjningen för UAV:er, ökar de samtidigt störningen som UAV:er orsakar för markbundna UE. I scenarier som involverar flera UAV:er kan LoS-anslutning till och med leda till störningsproblem sinsemellan. Dessutom leder LoS-anslutning till omfattande överlappande täckningsområden för flera BS:er för UAV, vilket tvingar dem att utföra frekventa överlämningar under flygningen om den mottagna signalstyrkan (RSS)-baserad överlämningspolicy används. UAV:s banor och BS-associationer, tillsammans med deras radioresursallokering, är väsentliga designparametrar som syftar till att möjliggöra deras sömlösa integrering i cellulära nätverk, med särskilt fokus på att hantera störningsnivåer de genererar och minska de redundanta handovers de utför. Därför designar denna avhandling två gemensamma bana och handover-hanteringsmetoder för en-UAV-respektive multi-UAV-scenarier, som syftar till att minimera den viktade summan av tre nyckelprestandaindikatorer (KPI:er): överföringsfördröjning, upplänksinterferens och överlämningsnummer . Tillvägagångssätten är baserade på ramverk för djup förstärkning inlärning (DRL) med duellerande dubbla djupa Q-nätverk (D3QN) och Q-lärande med ett MIXer-nätverk (QMIX) algoritmer som väljs som träningsagenter. Valet av dessa DRL-algoritmer motiveras av deras förmåga att utforma sekventiella beslutsfattande policyer som består av banadesign och handover-hantering. Resultaten visar att de föreslagna tillvägagångssätten effektivt tar itu med ovannämnda utmaningar samtidigt som de säkerställer den låga överföringsfördröjningen för mobilanslutna UAV:er. Dessa resultat står i kontrast till prestanda för benchmark-schemat, som styr UAV:er att följa den kortaste vägen och utföra överlämningar baserat på RSS. Närmare bestämt, när man överväger singel-UAV-scenariot, minskar det D3QN tillvägagångssättet upplänksinterferensen med 18% och överlämningssiffrorna med 90% med en 59% ökning av överföringsfördröjningen jämfört med riktmärket. Den ekvivalenta fördröjningsökningen är 15 mikrosekunder, vilket anses vara försumbart. För scenariot med flera UAV:er optimerar det QMIX-baserade tillvägagångssättet tillsammans tre prestandamått jämfört med benchmark-schemat, vilket resulterar i en 70% minskning av störningar, en 91% minskning av överlämningssiffror och en 47% minskning av överföringsfördröjningen. Det är anmärkningsvärt att en ökning av UAV:er som arbetar inom samma nätverk leder till prestandaförsämring på grund av UAV:er som konkurrerar om kommunikationsresurser och ömsesidig störning. Vid övergången från scenariot med en UAV till scenariot med flera UAV, upplever prestanda för benchmark-schemat en betydande nedgång, med en ökning på 199% av störningar, 89% i överlämnandetal och 652% i överföringsfördröjning. Däremot koordinerar den föreslagna QMIX-algoritmen effektivt flera UAV, vilket minskar prestandaförsämring och uppnår prestanda liknande D3QN-algoritmen som tillämpas i single-UAV-scenariot: en störningsökning på 9%, en ökning av antalet överlämningar med 9% och en fördröjningsökning på 152%. Ökningen av fördröjningen tillskrivs de minskade kommunikationsresurserna tillgängliga för varje enskild UAV, givet nätverkets konstanta kommunikationsresurser.

Sjöräddning i Sverige sker genom samverkan mellan statliga verksamheter, kommuner och frivilligorganisationer för ett gemensamt mål att rädda personer som råkat i sjönöd. Tid är ofta en kritisk faktor i räddningsuppdragen men ett snabbt och oplanerat utryck riskerar samtidigt att sätta räddningsaktörerna själva i farozonen. Utvecklingen av obemannade autonoma farkoster för SAR (eng. Search And Rescue) ses som en lösning på behovet att kunna snabbt skicka hjälp till samt få ögon på incidentplatsen utan att försätta räddningsaktörerna för onödig risk. Nuvarande kommunikationssystem inom svensk sjöräddning kan dock inte hantera annan typ av information än muntlig varav räddningsaktörer endast känner till riktlinjer för hantering av muntlig information. Med ett framtida införande av autonoma farkoster kommer dock fler informationstyper att behöva hanteras i sjöräddningar varav oklarheten om vilka informationstyper autonoma farkoster samlar in och vilka datahanteringskrav som finns är problematiskt. Oklarhet om informationstyperna och deras datahanteringskrav är vidare problematiskt för utvecklingen och implementeringen av autonoma farkoster då risken finns att farkoster och tekniker utvecklas men inte får användas för att de inte är anpassade efter lagkraven på hantering av olika datatyper. I denna studie undersöks därför vilka informationstyper som autonoma farkoster kan samla in vid sjöräddning. Detta för att komma fram till vilka riktlinjer för datahantering som gäller vid sjöräddning med autonoma farkoster. Studien undersöker också vilka informationstyper som är kritiska för en SAR-sjöräddningssamverkan samt vilka informationsdelningsutmaningar som finns i dagens sjöräddning. Studien genomfördes i form av en kvalitativ fallstudie och har tillämpat ett socio-tekniskt systemperspektiv för att bättre se till helheten och besvara frågeställningarna. Resultatet av denna studie visar att autonoma farkoster kan samla in information om sin omgivning, vilket utgör grunden för att skapa en medvetenhet om situationen som är kritiskt för SAR-operationer, och kan även samla in information om sitt eget tillstånd. De lagverk som identifierats utgöra de huvudsakliga restriktionerna är kamerabevakningslagen, lagen för skydd av geografisk information, offentlighets- och sekretesslagen, GDPR och dataskyddslagen. Dessa lagverk innehåller riktlinjer för delning av information och personuppgiftsbehandling i SAR-sjöräddning. Kunskapsbidrag studien har genererat inkluderar bland annat identifiering av datatyper som kan samlas in av autonoma farkoster i en SAR-sjöräddning, och sannolikt andra typer av räddningsinsatser, och delning och hanteringskraven på de datatyperna i räddningsinsatser och därmed kunskap om vilka datatyper som är mest reglerade. Vidare kunskapsbidrag är kunskap om vilka informationstyper som är mest kritiska för SAR-sjöräddningar, och därför bör prioriteras att samlas in och delas, och identifieringen av utmaningar för informationsdelning mellan statliga verksamheter och frivilligorganisationer. / Maritime rescue in Sweden is performed through a cooperation between government agencies, municipalities and non-governmental organisations (NGOs) with the common goal of saving people in distress. Time is often a critical factor in the rescue missions but a fast and unplanned response may at the same time put the rescue workers in danger. The development of unmanned autonomous vehicles for SAR is seen as a solution to the need of being able to quickly sendhelp as well as get eyes on the scene of the incident without exposing the rescue workers for unnecessary risks. However, the current communications systems in Swedish maritime rescue are unable to handle any other type of information except verbal, meaning that rescue workers only know guidelines for handling verbal information. However, with a future implementation of autonomous vehicles, there will be a need to handle more information types in maritime rescue whereas the uncertainty regarding what kind of information autonomous vehicles collect and which data management requirements exist is problematic. The uncertainty about the information types and their data management requirements is also problematic for the development and implementation of autonomous vehicles as there is a risk that vehicles and technologies are developed but not allowed to be used because they are not adapted to the legal requirements on management of the different types of data. Therefore, in this study I examine what information types that autonomous vehicles can collect in a maritime rescue to find out what guidelines for data management that applies during a maritime rescue with autonomous vehicles. The study also examines what kind of information’s are critical for a SAR maritime rescue cooperation as well as what information sharing challenges exist in current maritime rescue. The study was performed as a qualitative case study and has used a socio-technical systems perspective so as to better see the overall picture and answer the research questions. The result shows that autonomous vehicles can collect information about their surroundings, which is the foundation for establishing situation awareness that is critical for SAR-operations, and that they can collect information about their own status. The main laws and regulations that have been identified as constituting the main restrictions are (translated from Swedish) the law of camera surveillance, the law for protection of geographical information, the public access to information and secrecy act, the GDPR and the data protection act. These contains guidelines for sharing information and the processing of personal data in SAR maritime rescue. The knowledge contributions of this study includes among others the identification of datatypes that can be collected by autonomous vehicles in SAR maritime rescue, and probably other types of rescue operations, and the sharing and management requirements on those datatypes in rescue operations and thus knowledge of what datatypes that are the most restricted. Further knowledge contributions is knowledge about which information types that are the most critical for SAR maritime rescue and thus should be prioritised for collection and sharing as well as the identification of challenges for information sharing between government agencies and NGOs.

Taking as its starting point Nietzsche’s and Foucault’s theses on liberalism and war, and Dillon and Reid’s extensive engagement thereof, this thesis offers a critical conceptualization of drones and drone warfare. I argue that deployment of drones specifically over and against bodies and communities in conflict zones in and between Afghanistan, Pakistan, Iraq, Yemen, Somalia, and until recently, Libya, is the material practice of a legal and political doctrine and precedent that has been established and policed most prominently by the United States and its military and intelligence apparatuses since the end of the Cold War. This novel precedent, however - due to its necessarily mutually constitutive relationship with a perceived danger said to be emerging from specific spaces, bodies, and communities in the decolonized and still-colonized worlds - locates its ontological and thus political genealogy in the anthropological knowledge that legally justified the (in)humanity of peoples and communities in these spaces during the era of high imperialism that lasted roughly from the nineteenth to mid-twentieth centuries. I theorize this as a mode of political, tragic nihilism through a reading of some key theories of Deleuze and Guattari, Foucault, and Nietzsche and specifically, their import to the field of critical security and international relations theory. I demonstrate that the semiotic image of the drone is a highly pertinent point of departure through which we can understand these political stakes of strategic discourses enunciating the imperatives of both the Revolution in Military Affairs as well as recent global counterinsurgency/counterterrorism operations, specifically as they relate to claims about what it is drones are said to productively offer such militaristic projects. Ultimately, I argue that it is through the semiotic image of the drone as a clean, precise tactic that furthers the strategic goals of counterterrorism to target specific bodies that we can begin to politically theorize a particularly malignant political nihilism symptomatic of contemporary liberal societies. However, I also suggest that it is through Nietzsche’s politics of nihilism that we can begin to think about radical critical interventions that resist such a dangerous mode of politics. / Graduate

Vision-based conventional landing of a fixed wing UAV is addressed in this thesis. The work includes mathematical modeling, interface to a software for rendering the outside scenery, image processing techniques, control law development and outdoor experimentation. This research focuses on detecting the lines or the edges that flank the landing site, use them as visual cues to extract the geometrical parameters such as the line co-ordinates and the line slopes, that are mapped to the control law, to align and conventionally land the fixed wing UAV. Pre-processing and image processing techniques such as Canny Edge detection and Hough Transforms have been used to detect the runway lines or the edges of a landing strip. A Vision-in-the-Loop Simulation (VILS) set up on a personal computer or laptop, has been developed, without any external camera/equipment or networking cables that enables visual serving toper form vision-based studies and simulation. UAV mass, inertia, engine and aero data from literature has been used along withUAV6DOF equations to represent the UAV mathematical model. The UAV model is interfaced to a software using UDP data packets via ports, for rendering the outside scenery in accordance with the UAV’s translation and orientation. The snapshots of the outside scenery, that is passed through an internet URL by including the ‘http’ protocol, is image processed to detect the lines and the line parameters for the control. VILS set has been used to simulate UAV alignment to the runway and landing. Vision-based alignment is achieved by rolling the UAV such that the landing strip that is off center is brought to the center of the image plane. A two stage proportional aileron control input using the line co-ordinates, bringing the midpoints of the top ends of the runway lines to the center of the image, followed by bringing the mid points of the bottom ends of the runway lines to the center of the image has been demonstrated through simulation. A vision-based control for landing has been developed, that consists of an elevator command that is commiserate with the acceptable range of glide slope followed by a flare command till touch down, which is a function of the flare height and estimated height from the 3rd order polynomial of the runway slope obtained by characterization. The feasibility of using the algorithms for a semi-prepared or unprepared landing strip with no visible runway lines have also been demonstrated. Landing on an empty tract of land and in poor visibility condition, by synthetically drawing the runway lines based on a single 3rd order slope. vs height polynomial solution are also presented. A fixed area, and a dynamic area search for the Hough peaks in the Hough accumulator array for the correct detection of lines are addressed. A novel technique for crosswind landing, quite different from conventional techniques, has been introduced, using only the aileron control input for correcting the drift. Three different strategies using the line co-ordinates and the line slopes, with varying levels of accuracy have been presented and compared. About 125 landing data of a manned instrumented prototype aircraft have been analysed to corroborate the findings of this research. Outdoor experiments are also conducted to verify the feasibility of using the line detection algorithm in a realistic scenario and to generate experimental evidence for the findings of this research. Computation time estimates are presented to establish the feasibility of using vision for the problem of conventional landing. The thesis concludes with the findings and direction for future work.

Autonomous aerial vehicles like missiles and unmanned aerial vehicles (UAVs) have attracted various military and civilian applications. The primary guidance objective of any autonomous vehicle is to reach the desired destination point (target or waypoint). However, many practical engagements impose additional constraints like minimum control effort, a desired final velocity direction or a predefined engagement time. This thesis addresses engagement time constrained guidance problems pertaining to missiles and UAVs. The first part of the thesis discusses a nonlinear guidance law for impact time control of missiles against stationary target. The guidance law is designed with a particular choice of missile heading error variation as a function of ran to-target. The proposed heading error variation leads to an exact closed-form expression for the impact time. controlling the impact time, a closed-form relation is derived relating the control parameter to the desired impact time. A new Lyapunov based guidance law with a monotonically decreasing lateral acceleration is proposed in the next part of the thesis. An exact expression for impact time with minimum and maximum achievable impact times is derived. A control parameter is proposed with a closed-form relationship to the desired impact time. Using the concept of predicted interception point, the two guidance laws are extended for impact time control against non-maneuvering and moving targets. The proposed guidance models are extended to three-dimensional engagements by deducing yaw and pitch lateral accelerations satisfying the desired heading error profile. Extensive simulation studies are carried out for single missile and salvo attack scenarios. The last part of the thesis presents a guidance methodology governing the arrival time of a UAV at a waypoint. A specific arrival angle is considered as an additional constraint. The arrival constraints are satisfied by varying the navigation gain of the proportional navigation guidance law. The methodology is applied for simultaneous and sequential arrival of UAVs at a waypoint.

It is a well-recognized fact that unmanned aerial vehicles are an essential element in today’s network-centric integrated battlefield environment. Compared to solo UAV missions, multiple unmanned aerial vehicles deployed in co-operative mode, offer many advantages that has motivated UAV researchers all over the world to evolve concept of operations that aims in achieving a paradigm shift from traditional ”dull” missions to perform ”dirty” and ”dangerous” missions. In future success of a mission will depend on interaction among UAV groups with no interaction with any ground entity. To reach this capability level, it is necessary for researchers, to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. The thesis is in four parts: (i) Development of an organized framework to realize the goal of achieving fully autonomous systems. (ii) Design of UAV grouping algorithm and coordination tactics for ground attack missions. (iii) Cooperative network management in GPS denied environments. (iv) UAV group tactical path and goal re-plan in GPS denied wide area urban environments. This research thesis represents many first steps taken in the study of autonomous UAV systems and in particular group autonomy. An organized framework for autonomous mission control level by defining various sublevels, classifying the existing solutions and highlighting the various research opportunities available at each level is discussed. Significant contribution to group autonomy research, by providing first of its kind solution for UAV grouping based on Dubins’ path, establishing GPS protected wireless network capable of operating in GPS denied environment and demonstration of group tactical path and goal re-plan in a layered persistent ISR mission is presented. Algorithms discussed in this thesis are generic in nature and can be applied to higher autonomous mission control levels, involving strategic decisions among UAVs, satellites and ground forces in a network centric environment.