Since the early 2000s the space industry has undergone significant changes such as the advent of reusable launch vehicles and an increase of commercial opportunities. This new space age is characterized by a dynamic entrepreneurial climate, lowered barriers to access space and the emergence of new markets. New business models are being developed by many actors and the merging of space and other sectors continues, facilitating innovative and disruptive opportunities. Already established companies are adapting in various ways as efforts to stay relevant are gaining attention. The previous pace of development that was exclusively determined by governmental programs are now largely set by private and commercial ventures. Relating to all trends, new technologies and driving forces in the space industry is no trivial matter. By analyzing and examining identified trends and technologies the author has attempted to discern those that will have a significant impact on the industrial environment during the next decade. Market assessments have been summarized and interviews have been carried out. Discussions and conclusions relating to the services provided by the Swedish Space Corporation are presented. This report is intended to update the reader on the current status of the space industry, introduce concepts and provide relevant commentary on many important trends. / Sedan början av 2000-talet har det skett markanta förändringar inom rymdindustrin, såsom utvecklingen av återanvändningsbara raketer och en ökad mängd kommersiella möjligheter. Denna nya rymder a karaktäriseras av ett dynamiskt klimat för entreprenörer, minskande barriärer för att etablera rymdverksamhet och uppkomsten av nya marknader. Nya affärsmodeller utvecklas och integrering mellan rymden och andra industrier fortsätter, vilket ger utrymme för utveckling av innovativa och disruptiva idéer. Redan etablerade företag anpassar sig till förändringarna på olika sätt och ansträngningar för att bibehålla relevans prioriteras. Utvecklingstakten inom branschen var tidigare dominerad av statliga program men är nu alltmer influerad av privata och kommersiella satsningar. Att relatera till ny teknik, nuvarande trender och drivkrafter inom rymdindustrin är Jacob Ask is pursuing a Master of Science degree in Aerospace Engineering at KTH Royal Institute of Technology in Stockholm, Sweden. Christer Fuglesang is a professor in Space Travel, director of KTH Space Center and responsible for the Aerospace Engineering master program. He serves as the examiner for this master thesis project. Linda Lyckman is the Head of Business & Technology Innovation at SSC and supervisor for this master thesis project. komplext. Genom att undersöka och analysera identifierade trender och teknologier ämnar författaren urskilja de som kan komma att påverka industrin i störst utsträckning under det kommande decenniet. Bedömningar av marknadsmöjligheter och intervjuer har genomförts och i denna rapport presenteras ¨aven diskussioner och slutsatser relaterade till den typ av tjänster som Swedish Space Corporation erbjuder. Denna rapport har för avsikt att uppdatera läsaren om delar av den aktuella nulägesanalysen inom rymdindustrin, introducera koncept och ge relevanta kommentarer om viktiga trender.

<p dir="ltr">As traffic in cislunar space grows and missions become increasingly complex, effective understanding of relative motion between spacecraft is paramount. Additional tools are necessary to more accurately determine favorable relative behaviors in a multi-body gravitational environment. In this investigation, relative states are characterized as linear combinations of principal stretching and restoring directions to introduce techniques for relative trajectory design in the circular restricted three-body problem. These dynamically-informed directions form sets of orthonormal vector bases employed to describe and design spacecraft motion relative to a reference trajectory. Properties unique to 3x3 subsets of the state transition matrix are exploited to derive methodologies for two relative motion applications. First, the formulation is presented from a spacecraft loitering perspective, providing a framework to methodically determine ballistic relative trajectory options for visiting spacecraft. Secondly, modifications are introduced for flexible maneuver planning in a collision avoidance scenario. The two methodologies are assessed for a variety of test cases along four different periodic orbits in the Earth-Moon system.</p>

<p dir="ltr">Tracking and characterizing the space debris population in Earth orbit is necessary to ensure that space can continue to be used safely. However, because space objects are affected by non-conservative forces like drag and solar radiation pressure, predicting the long-term evolution of their orbits is impossible without knowledge of their attitude profiles. Such knowledge may be unavailable for inactive satellites or objects of which the observer is not the owner or operator. In many cases, attitude cannot be measured directly because resolved images of space objects are unavailable due to the distance between the object and the observer, and the effects of atmospheric seeing. However, the total brightness of objects can still be measured. A set of brightness measurements over time is referred to as a "light curve.'' An object's observed brightness is influenced by its attitude and other factors such as its orbit, shape, and reflective properties. If some of these other factors are known, attitude information may be extracted from a light curve. Existing methods of solving this attitude inversion problem either require a good initial guess for an object's rotational states or do not provide a full state estimate. The work in this thesis avoids both problems and provides a full state estimate without requiring an initial state guess.</p><p><br></p><p dir="ltr">The attitude estimation process assumes that the observation geometry and the observed object's shape, reflection properties, and inertia tensor are known. In this thesis, an initial method of searching for attitudes that could correspond to each measurement using the viewing sphere is described. These possible attitudes or "pseudo-measurements'' are then used to initialize a probability hypothesis density filter that is theoretically capable of representing the multi-modal nature of the attitude estimate using a Gaussian mixture model. However, the probability hypothesis density filter is found to often diverge from the truth because it is necessary to merge and prune components of the Gaussian mixture model to avoid computational intractability. In its place, a particle swarm optimizer method for performing an attitude inversion has been developed. This method uses analytic attitude solutions to quickly propagate a large number of attitude time histories simultaneously. The particle swarm optimizer method is validated using simulated light curves for several objects. A preliminary attempt is made to estimate the attitude of an object using real light curve measurements.</p>

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<p dir="ltr">The recent and upcoming increase in spaceflight missions to the lunar vicinity necessitates methodologies to enable operations beyond the Earth. In particular, there is a pressing need for a Space Domain Awareness (SDA) and Space Situational Awareness (SSA) architecture that encompasses the realm of space beyond the sub-geosynchronous region to sustain humanity's long-term presence in that region. Naturally, the large distances in the cislunar domain restrict access rapid and economical access from the Earth. In addition, due to the long ranges and inconsistent visibility, the volume contained within the orbit of the Moon is inadequately observed from Earth-based instruments. As such, space-based assets to supplement ground-based infrastructure are required. The need for space-based assets to support a sustained presence is further complicated by the challenging dynamics that manifest in cislunar space. Multi-body dynamical models are necessary to sufficiently model and predict the motion of any objects that operate in the space between the Earth and the Moon. The current work seeks to address these challenges in dynamical modeling and cislunar accessibility via the exploration of resonant orbits. These types of orbits, that are commensurate with the lunar sidereal period, are constructed in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP) and validated in the Higher-Fidelity Ephemeris Model (HFEM). The expansive geometries and energy options supplied by the orbits are favorable for achieving recurring access between the Earth and the lunar vicinity. Sample orbits in prograde resonance are explored to accommodate circumlunar access from underlying cislunar orbit structures via Poincaré mapping techniques. Orbits in retrograde resonance, due to their operational stability, are employed in the design of space-based observer constellations that naturally maintain their relative configuration over successive revolutions. </p><p dir="ltr"> Sidereal resonant orbits that are additionally commensurate with the lunar synodic period are identified. Such orbits, along with possessing geometries inherent to sidereal resonant behavior, exhibit periodic alignments with respect to the Sun in the Earth-Moon rotating frame. This characteristic renders the orbits suitable for hosting space-based sensors that, in addition to naturally avoiding eclipses, maintain visual custody of targets in the cislunar domain. For orbits that are not eclipse-favorable, a penumbra-avoidance path constraint is implemented to compute baseline trajectories that avoid Earth and Moon eclipse events. Constellations of observers in both sidereal and sidereal-synodic resonant orbits are designed for cislunar SSA applications. Sample trajectories are assessed for the visibility of various targets in the cislunar volume, and connectivity relative to zones of interest in Earth-Moon plane. The sample constellations and observer trajectories demonstrate the utility of resonant orbits for various applications to sustain operations in cislunar space. </p>

The increasing reliance on space infrastructure and its rapid expansion necessitate the development and enhancement of tools for space debris and fragmentation research. Accurate prediction of the risks associated with satellite fragmentation requires comprehensive understanding of the dynamics involved. To address this need, the widely used NASA Standard Breakup Model (SBM) is employed in this thesis to predict fragment characteristics resulting from breakup events. Additionally, a novel method is introduced to determine the direction of these fragments, something not directly covered by the SBM. Furthermore, the principle of kinetic gas theory is applied to calculate the overall, long-term collision risk between debris and a predetermined satellite population. The results from this reveal the limitations of the SBM in accurately simulating fragmentations for certain satellite types. However, the newly implemented fragment directionality method aligns well with observed data, suggesting its potential for further research. Similarly, the risk model exhibits strong correspondence with ESA's MASTER, a model used for assessing collision risks with debris, with the deviations likely due to different impact velocity models used. Finally, the validated fragmentation and risk models are combined, and the combined model is used to analyse a real-world fragmentation event. / Det ökande beroendet av rymdinfrastruktur, samt dess snabba expansion kräver utveckling och förbättring av verktyg för forskning och analys kring rymdskräp och fragmentering. För att förstå risken förknippad med satellitfragmentationer så krävs förståelse för den involverade dynamiken. För att tillgodose detta används NASA:s Standard Breakup Model (SBM) i denna avhandling för att bestämma fragmentegenskaper som bildas från olika sorters fragmentationshändelser. Dessutom introduceras en ny metod för att bestämma riktningen för dessa fragment, något som inte direkt täcks av SBM. Dessutom tillämpas principen för kinetisk gasteori för att beräkna den totala, långsiktiga kollisionsrisken mellan rymdskrot och en förutbestämd satellitpopulation. Resultaten från detta avslöjar SBM:s begränsningar när det gäller att simulera fragmenten för vissa satellittyper. Hursomhelst så kan man se att den nyligen implementerade fragmentriktningsmetoden stämmer väl överens med den observerade datan, vilket tyder på dess potential för ytterligare forskning. På samma sätt uppvisar riskmodellen överensstämmelse med ESA:s MASTER, en modell som används för att bedöma kollisionsrisker med rymdskrot, där avvikelser sannolikt beror på att olika kollisionshastighetmodeller används. Slutligen kombineras de validerade fragmenterings- och riskmodellerna, som sedan används för att bidra med analyser till en riktig fragmentationshändelse.

Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. The performanceper-watt and flexibility of reconfigurable Field-Programmable Gate Arrays (FPGAs) make them a good candidate for deep learning model deployment in space, supporting in-flight updates and maintenance. However, the FPGA design costs of custom accelerators for complex algorithms remains high. The research focus of the thesis relies on novel high-level synthesis (HLS) workflows that allow the developer to raise the level of abstraction and lower design costs for deep learning accelerators, particularly for space-representative applications. To this end, four different hardware accelerators of convolutional neural network models for spacebased debris detection are implemented (ResNet, SqueezeNet, DenseNet, TinyCNN), using the open-source HLS tool NNgen. The obtained hardware accelerators are deployed to a reconfigurable module of the Zynq Ultrascale+ MPSoC programmable logic, and compared in terms of inference performance, resource utilization and latency. The tests on the target hardware show a detection accuracy over 95% for ResNet, DenseNet and SqueezeNet, and a localization intersection-over-union over 0.5 for the deep models, and over 0.7 for TinyCNN, for space debris objects at a range between 1km and 100km for a diameter of 1cm, or between 100km and 1000km for a diameter of 10cm. The obtained speed-ups with respect to software-only implementations lay between 3x and 32x for the different hardware accelerators. / Rymdskrot och rymdsituationstänksamhet (SSA) har blivit växande oro för nationell säkerhet och hållbarheten för rymdoperationer, där snabb upptäckt och spårning av rymdobjekt är avgörande för att förhindra kollisioner. Traditionella datorseendealgoritmer har använts omfattande för att lösa problem med upptäckt och spårning i flygning, men på senare tid har djupinlärningsmetoder fått stor användning inom icke rymdrelaterade applikationer på grund av sin höga noggrannhet. Prestandaper-watt och flexibiliteten hos omkonfigurerbara Field-Programmable Gate Arrays (FPGAs) gör dem till en bra kandidat för distribution av djupinlärningsmodeller i rymden, med stöd för uppdateringar och underhåll under flygning. Men FPGAdesignkostnaderna för anpassade acceleratorer för komplexa algoritmer är fortfarande höga. Forskningsfokus för avhandlingen ligger på nya högnivåsyntes (HLS) arbetsflöden som gör det möjligt för utvecklaren att höja abstraktionsnivån och sänka designkostnaderna för acceleratorer för djupinlärning, särskilt för tillämpningar i rymden. För detta har fyra olika hårdvaruacceleratorer för modeller av konvolutionsnätverk för upptäckt av rymdbaserat skrot implementerats (ResNet, SqueezeNet, DenseNet, TinyCNN), med hjälp av öppen källkod HLS-verktyget NNgen. De erhållna hårdvaruacceleratorerna distribueras till en omkonfigurerbar modul av Zynq Ultrascale+ MPSoC-programmerbar logik och jämförs med avseende på inferensprestanda, resursutnyttjande och latens. Testerna på målhardwaren visar en upptäktnoggrannhet på över 95% för ResNet, DenseNet och SqueezeNet, och en lokaliserings-intersektion-över-union på över 0,5 för de djupa modellerna och över 0,7 för TinyCNN för rymdskrotobjekt på en avstånd mellan 1 km och 100 km för en diameter på 1 cm eller mellan 100 km och 1000 km för en diameter på 10 cm. De erhållna hastighetsökningarna i förhållande till endast programvara ligger mellan 3x och 32x för de olika hårdvaruacceleratorerna.