1 Unified Classification for Distributed Satellite Systems Abstract
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Unified Classification for Distributed Satellite Systems A. Poghosyana*, I. Llucha, H. Matevosyana, A. Lamba, C.A. Morenoa, C. Taylora, A. Golkara, J. Coteb, S. Mathieub, S. Pierottib, J. Graveb, J. Narkiewiczc, S. Topczewskic, M. Sochackic, E. Lancherosd, H. Parkd, A. Campsd a Skolkovo Institute of Science and Technology, Moscow, Russia b Thales Alenia Space, Cannes, France c Politechnika Warszawska, Warsaw, Poland d Universitat Politecnica de Catalunya, Barcelona, Spain * [email protected] Abstract: Recent technological advances spurred the exploration of problems that require multiple spacecraft operating in a synchronized manner, promoting research and development activities in innovative distributed space system concepts including Constellations, Trains, Clusters, Swarms, Fractionated Satellites, and Federated Satellites. The increasing interest in distributed space systems necessitates a unified classification framework. This report provides a detailed classification for different distributed architectures as well as clarifies the distinctions in terms of mission goals, level of cooperation required for accomplishing the mission objectives, level of homogeneity between individual spacecraft or fractions of the distributed spacecraft, inter-satellite distance, and level of autonomy of spacecraft or a fraction of distributed spacecraft. Thus giving a clear outline for consistently defining various distributed architectures independent of the individual mission framework. Introduction Over the past 60 years, the space industry has advanced engineering principles that provide large, expensive and optimal satellites handcrafted by large groups of engineers, based on tightly coupled subsystems and designed to accomplish a set of mission goals satisfying particular user needs. However, recent technological advances spurred the exploration of problems that require multiple spacecraft operating in a synchronized manner. Starting from 1980s numerous multi-spacecraft missions were proposed and implemented including GPS navigation constellation [1], Iridium [2] and Globalstar [3] communication constellations, NASA’s Afternoon Constellation (A-Train) for Earth observation [4] or the COSMIC Constellation for GNSS-Radio Occultations [5] as well as TDRSS [6] and EDRS [7] geostationary data relay satellites. As highlighted by I. Lluch and A. Golkar [8], “the last decade has seen the rise of a variety of novel space system architecture proposals with a focus on distribution”. Distributed Satellite Systems (DSS) are defined as mission architectures consisting of multiple space elements that interact, cooperate and communicate with each other, usually resulting in new system properties and/or emerging functions. Generally, distributed architectures are categorized by a variety of names including Constellations, Trains, Clusters, Swarms, Fractionated Satellites, and Federated Satellites (Table 1). For example, the constellation is a traditional approach, when sporadically distributed satellites are used for maximizing the coverage. On the other hand, clusters are deliberately positioned closely together for enhancing or creating new system capabilities, thus requiring very precise attitude determination and control in order to maintain the formation stability as well as to avoid satellite collisions. Swarms are roughly comparable to clusters except they involve a much larger number of usually smaller and cheaper satellites. Additionally, swarms do not have as stringent attitude determination and control requirements as the clusters [9]. 1 Fractionated spacecraft [10] and Federated Satellite Systems [11] are relatively novel concepts involving a network of multiple heterogeneous space elements that interact, cooperate and communicate with each other, creating new emerging capabilities. With a notable exception of multistatic radar and distributed aperture missions, these novel DSS architectures provide new system attributes rather than enabling breakthrough functionalities. These attributes come at the cost of increased complexity in terms of interfacing, synchronization and networking, which are more mature or less complex in monolithic systems [8]. Table 1 Types of distributed mission architectures [2, 4, 12-15]. DSS Inter-Satellite Mission goals Cooperation Homogeneity Autonomy architectures distance Homogeneous Mission goal Cooperation required components, some Constellations shared (Iridium, to support mission Regional Autonomous differences possible GPS) goals (GPS generations) Independent, but Cooperation from Heterogeneous Trains Local Autonomous could be shared optional to required components Cooperation required Autonomous Mission goal Homogeneous Clusters to support mission Local to completely shared components goals co-dependent Cooperation required From homogeneous Autonomous Mission goals From local to Swarms to support mission to heterogeneous to completely shared regional goals components co-dependent From optional Autonomous Fractionated Mission goals (service areas) to Heterogeneous Local to completely Satellites shared required (distributed components co-dependent critical functions) Federated Independent Heterogeneous From local to Ad-hoc, optional Autonomous Satellites mission goals components regional Several distributed architectures could be classified as formation-flying missions. Notable examples of formation-flying missions include TerraSAR-X – TanDEM-X [16], GRACE [17], and PRISMA [18]. Formation flight involves some form of tight flight control compared to constellations and it is deployed responding to a cohesive mission need, requiring cooperation to achieve it. Figure 1 shows a notional representation of satellite formations, fractionated spacecraft and satellite federations, compared to monolithic spacecraft and constellations. Detailed descriptions of different DSS architecture types are presented in the Table 1 as well as in the subsequent sections. Homogeneity in Table 1 is defined as the level of similarity between individual spacecraft or fractions of the distributed spacecraft, and Autonomy is defined as the level of operational independence of the spacecraft or a fraction of distributed spacecraft. This work was conducted in the framework of the ONION “Operational Network of Individual Observation Nodes” project supported by the European Union’s Horizon 2020 research and innovation programme. The goal of ONION is to propose a pragmatic, evolutionary and scalable approach, hybridizing fractionated and federated satellite system concepts, and augmenting existing space assets for the development of future space missions and new services. Thus, the objective of this paper is to provide a unified classification framework for consistently defining various distributed architectures independent of individual missions. 2 Figure 1 Notional representation of satellite formations, fractionated spacecraft and satellite federations compared to monolithic spacecraft and constellations. A single mission, termed ‘A’ can be performed by a monolithic satellite, or either require a constellation or a formation. A fractionated spacecraft is a breakdown of the components to carry out mission A, while in a Federation different missions (A, B, C, D) cooperate. Constellations Satellite constellations were the first successful implementation of distributed satellite systems, which respond to a functional need such as continuous real time global coverage. In contrast, other novel DSS are generally more oriented to achieve new properties or attributes such as added flexibility or robustness for the overall system. Satellite constellations have multiple applications such as communication, navigation, and Earth science [19]. To date, many constellations successfully accomplished their technical objectives, but some of the commercial constellations have not been financially successful due to increasing competition from terrestrial infrastructure and suboptimal user-base penetration [20] as in the case of Iridium and Teledesic in the 1990s. Prominent examples of satellite constellation include GPS [1], GLONASS [21], Galileo [22] and Beidou [23] global navigation satellite systems, Iridium [2], Iridium NEXT [24], Globalstar [3] and O3b [25] communication constellations, DMC [26] and Flock [27] Earth observation constellations. It is evident that the space industry is experiencing an increased shift of interest from large and expensive satellites handcrafted by large groups of engineers to a smaller, cheaper, mass-produced satellites over the past decades. Such trend has resulted in a revived implementation of ambitious ideas such as megaconstellations. Compared to previous attempts at establishing large in-space constellations (such as the case of Teledesic) new opportunities have been opened due to the increased pervasiveness of Internet to everyday life and business operations, and increased cost efficiency and performance associated with small satellite systems. Currently, megaconstellations are one of the hottest trends in the space communications industry. They are envisioned to make a major breakthrough in affordable global broadband capacity and other services – although no demonstration of the business case and its promises has been yet realized, to the date of writing this report. 3 OneWeb and LeoSat are two notable examples of communication megaconstellations currently under development [28, 29]. When completed, the OneWeb constellation will consist of 648 satellites with mass of less than 150 kg operating in