Pursuit-evasion game theoretic uncertainty oriented sensor management for elusive space objects

This paper develops and evaluates an orbital emulator (OE) for space situational awareness (SSA). The OE can produce 3D satellite movements using capabilities generated from omni-wheeled robot and robotic arm motion methods. The 3D motion of a satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The 3D actions are emulated by omni-wheeled robot models while the up-down motions are performed by a stepped-motor-controlled-ball along a rod (robotic arm), which is attached to the robot. For multiple satellites, a fast map-merging algorithm is integrated into the robot operating system (ROS) and simultaneous localization and mapping (SLAM) routines to locate the multiple robots in the scene. The OE is used to demonstrate a pursuit-evasion (PE) game theoretic sensor management algorithm, which models conflicts between a space-based-visible (SBV) satellite (as pursuer) and a geosynchronous (GEO) satellite (as evader). The cost function of the PE game is based on the informational entropy of the SBV-tracking-GEO scenario. GEO can maneuver using a continuous and low thruster. The hard-in-loop space emulator visually illustrates the SSA problem solution based PE game.

Resident Space Objects (RSOs) are a collection of both man-made and natural objects in near-Earth space. Given their large orbital velocities and rapidly increasing quantity, they pose a collision threat to space assets, necessitating better Space Situational Awareness (SSA). SSA begins with detecting these objects in the first place and can be accomplished by using space-based optical images, such as images from the Fast Auroral Imager (FAI) on the CASSIOPE satellite. However, these short-exposure images are low in resolution and contain various artifacts and noise, posing challenges to traditional source detection methods. Furthermore, the background stars and RSOs both move due to the satellite’s non-constant attitude, posing a challenge for tracking algorithms. Nevertheless, these images are a valuable source of SSA data, which can be used to develop algorithms to ultimately augment the capabilities of current SSA systems. Such augmentations include performing RSO detection as a simultaneous function on existing spacecraft or allowing dedicated SSA payloads to detect RSOs during slew maneuvers, where background stars will similarly move. This paper proposes a rules-based RSO tracking algorithm tailored for low-resolution, short-exposure, space-based imagery with non-constant spacecraft attitude, addressing the challenge of distinguishing RSOs from background stars that are also in motion. This method consists of a custom thresholding algorithm, along with the Iterative Closest Point (ICP) algorithm to correct the motion of the background stars, followed by a tracking algorithm to finally detect the RSOs within the imagery, returning their pixel positions. The algorithm was tested on an 878-image dataset, achieving 79% precision and 71% recall, while detecting 87% of all RSOs at least once. These results prove that the algorithm is a feasible method for detecting RSOs in non-constant-attitude imagery, providing a means to develop current SSA systems.

Space situational awareness (SSA) plays an important role in maintaining space advantages. Task planning is one of the key technologies in SSA to allocate multiple tasks to multiple satellites, so that a satellite may be allocated to supervise multiple space objects, and a space object may be supervised by multiple satellites. This paper proposes a hierarchical and distributed task-planning framework for SSA systems with focus on fast and effective task planning customized for SSA. In the framework, a global task-planner layer performs satellite and object clustering, so that satellites are clustered into multiple unique clusters on the basis of their positions, while objects are clustered into multiple possibly intersecting clusters, hence allowing for a single object to be supervised by multiple satellites. In each satellite cluster, a local task planner performs distributed task planning using the contract-net protocol (CNP) on the basis of the position and velocity of satellites and objects. In addition, a customized discrete particle swarm optimization (DPSO) algorithm was developed to search for the optimal task-planning result in the CNP. Simulation results showed that the proposed framework can effectively achieve task planning among multiple satellites and space objects. The efficiency and scalability of the proposed framework are demonstrated through static and dynamic orbital simulations.

Space situational awareness (SSA) plays an imperative role in maintaining safe space operations, especially given the increasingly congested space traffic around the Earth. Space-based SSA offers a flexible and lightweight solution compared to traditional ground-based SSA. With advanced machine learning approaches, space-based SSA can extract features from high-resolution images in space to detect and track resident space objects (RSOs). However, existing spacecraft image datasets, such as SPARK, fall short of providing realistic camera observations, rendering the derived algorithms unsuitable for real SSA systems. In this work, we introduce SpaceDet, a large-scale realistic space-based image dataset for SSA. We consider accurate space orbit dynamics and a physical camera model with various noise distributions, generating images at the photon level. To extend the available observation window, four overlapping cameras are simulated with a fixed rotation angle. SpaceDet includes images of RSOs observed from 19 km to 63,000 km, captured by a tracker operating in LEO, MEO, and GEO orbits over a period of 5,000 seconds. Each image has a resolution of 4418 x 4418 pixels, providing detailed features for developing advanced SSA approaches. We split the dataset into three subsets: SpaceDet-100, SpaceDet-5000, and SpaceDet-full, catering to various image processing applications. The SpaceDet-full corpus includes a comprehensive dataloader with 781.5 GB of images and 25.9 MB of ground truth labels. Furthermore, we adapted detection and tracking algorithms on the collected dataset using a specified splitting method to accelerate the training process. The trained model can detect RSOs from real-world space observations with zero-shot capability.

Space situational awareness (SSA) refers to collecting, analyzing, and keeping track of detailed knowledge of resident space objects (RSOs) in the space environment. With the rapidly increasing number of objects in space, the need for SSA grows as well. Traditional methods rely heavily on imaging RSOs from large, narrow field-of-view (FOV), ground-based telescopes. This research outlines the technology demonstration payload, Resident Space Object Near-space Astrometric Research (RSONAR)-a star tracker-like, wide FOV camera combined with commercial off-the-shelf (COTS) hardware to image RSOs from the stratosphere, overcoming the disadvantages of ground-based observations. The hardware components and software algorithm are described and evaluated. The eligibility of the payload for SSA is proven by the image processing algorithms, which detect the RSOs in the images captured during flight and the survival of the COTS components in the near-space environment. The payload features a low-resolution, wide FOV camera coupled with a Field Programmable Gate Array (FPGA)-based platform that houses the altitude and time-based image capture algorithm. The newly developed payload in a 2U-CubeSat form factor was flown as a space-ready payload on the CSA/CNES stratospheric balloon research platform to carry out algorithm and functionality tests in August 2022.

Space situation awareness (SSA) includes tracking of active and inactive\nresident space objects (RSOs) and assessing the space environment through\nsensor data collection and processing. To enhance SSA, the dynamic data-driven\napplications systems (DDDAS) framework couples on-line data with off-line\nmodels to enhance system performance. Using feedback control, sensor\nmanagement, and communications reliability. For information management, there\nis a need for identity authentication and access control to ensure the\nintegrity of exchanged data as well as to grant authorized entities access\nright to data and services. Due to decentralization and heterogeneity of SSA\nsystems, it is challenging to build an efficient centralized access control\nsystem, which could either be a performance bottleneck or the single point of\nfailure. Inspired by the blockchain and smart contract technology, this paper\nintroduces BlendCAC, a decentralized authentication and capability-based access\ncontrol mechanism to enable effective protection for devices, services and\ninformation in SSA networks. To achieve secure identity authentication, the\nBlendCAC leverages the blockchain to create virtual trust zones and a robust\nidentity-based capability token management strategy is proposed. A\nproof-of-concept prototype has been implemented on both resources-constrained\ndevices and more powerful computing devices, and is tested on a private\nEthereum blockchain network. The experimental results demonstrate the\nfeasibility of the BlendCAC scheme to offer a decentralized, scalable,\nlightweight and fine-grained access control solution for space system towards\nSSA.\n

The outer space domain has become increasingly congested. In order to responsibly operate space systems, owner/operators must track their satellites and maintain awareness of all other tracked space objects, and act to minimize any risks that can be identified. This has spawned an evolution of space systems operational practices. Commercial and other satellite owner/operators rely on space situational awareness (SSA) information needed to reduce the chances of collisions with other spacecraft or orbital debris. The United States (U.S.) Strategic Command (USSTRATCOM) has been authorized to make SSA information and services available to non-U.S. foreign and commercial entities. Responding to the needs, direct private SSA solutions also have been proposed to satisfy perceived needs in the operator marketplace.

The location and intensity of non-resolved space object can be obtained in space-based surveillance, however, the material, size, and status of the object are lost. Spectrum represents the inherent property difference of the object, which can be used as an important means for feature extraction and recognition of non-resolved space object. According to the influence factors, including material, structure, background and orbit, a mathematical model for spectral properties of space object is established. Based on the model, inverse calculation method for feature extraction and recognition of space object is proposed. Taking the HuanJing-1 satellite scale model as an example, experimental verification for feature extraction and recognition of space object in typical parameters is made. Experimental results demonstrate the validity of the modeling method.

Jena-Optronik GmbH (JOP) has a strong heritage in the development of cameras for numerous space-based applications. So far, our focus has been on cameras working in the visible & near-infrared range of the spectrum. The main application case for these cameras is the use as autonomous star trackers being part of a satellite’s Guidance, Navigation & Control System. JOP has been very successful with these cameras –for a long time and is the world market leader in providing such sensors to satellite prime manufactures. Investigating the possible applications of cameras in space, one realizes that thermal or long wave infrared (IR) cameras are necessary for specific applications. One major field for several of such applications is the field of Space Situational Awareness (SSA). SSA has been attracting more and more attention over the past years especially for space objects, natural debris as well as man-made threats & disturbances such as military conflicts. Two further application fields for IR cameras are formation flying and docking. The first rendezvous and docking with a non-cooperative satellite (Intelsat 901) in geostationary orbit by Northrop Grummans MEV-1 was a key milestone for docking capabilities. Additionally, the recent years have shown that a transition from few big and multi-purpose satellites towards small, medium or even large constellations consisting of numerous small satellites is taking place. Especially for constellations, a key aspect is formation flying which usually requires relative navigation. The relative angles between the satellites can be easily provided by IR cameras as they detect the satellites reliably and independently of local lighting conditions due to their elevated temperature against the cold space background. Based on the very successful ASTRO product range of space proven cameras, ASTROtir (tir = thermal infra-red) will extend JOPs product capabilities into the thermal / long wave infrared (LWIR) wavelength range to cover all of the applications cases listed above. Following a successful ESA co-funded first evaluation, the ASTROtir development project has been kicked-off in December 2022 in cooperation with ESA. Target of this project is to demonstrate TRL8 of ASTROtir. Future applications are formation flying (relative navigation), approach and docking with a non-cooperative space object as well as detailed image generation of near satellite objects. Currently, no European solution for an IR camera covering the application cases and mission scenarios is available as off-the-shelf product, which further increases ASTROtir’s already promising market potential. As the market demand, especially in the field of SSA, is rising further in the coming years, its development will be closely followed and its market entry is eagerly awaited. Jena-Optronik will present the preliminary design and development status of ASTROtir. As ASTROtir aims primarily at applications on small and medium satellites, its target mass budget of 300g is a driving requirement. Following our intermediate / preliminary design review scheduled for August 2023, Jena-Optronik aims at presenting first results from ASTROtir’s system analyses as well as the related plans for the EM and EQM campaigns.

For geosynchronous( GEO) orbit space object's detection and recognition is mainly dependent on analysis,identification and judgment of reflected sunlight photometric curve. Based on analysis of visible light reflection characteristics calculation model of space target surface diffuse reflection properties,a study on different shape reflector light reflection characteristics was carried out. Moreover,a different shape reflector method was proposed to control space target light reflection characteristics. Taking an example of a GEO satellite,simulate and analyze the effects of cone,sphere and cylinder as reflection control body on satellite reflection characteristics by using Monte Carlo non-sequential light tracing method. The results show that the method can change the satellite original visible light reflection curve,reduce the probability of being detected and recognized,and improve the invisibility of space object.

More than half a century of space activities has resulted in the placement of thousands of space objects in the Earth’s orbit. Understanding the questions of how, where, and who is moving in orbit is essential for the management of space infrastructure. Since the beginning of space missions, this task as been carried out by Space Situational Awareness (SSA) systems. SSA systems are made up of several subsystems, such as optical recognition instruments, radar and radio. In recent years, these terrestrial systems have also been joined by satellites with similar capabilities. Historically, these systems were maintained by the military. It was only recently that the private sector became a major actor in the field. Consultation, Command and Control (C3) allows for a strong collaboration of civil and military bodies that focus on information sharing and interoperability of the systems. SSA systems have proved critical in collisions between satellites and in preserving the structural stability of the International Space Station several times. The security of these systems hence has a strong impact on space and international security. The growing importance of the space sector and its progressive recognition as a critical infrastructure shows that a model and simulation of the scenario are essential for a centric defence. This paper will identify the areas that pose external threats focussing on the European and Arctic region. Following the threats’ evaluation, their level of disruption will be considered, whether partial or total, permanent or temporary. This approach will enable the development of a European and Arctic scenario analysis of SSA disruption and evaluate the SSA security in the coming years. The scenario will take into consideration environmental, kinetic, electronic, and cyber sources of disruption.

Space object identification is vital for operating spacecraft, space traffic control, and space situational awareness, but initial determination, maintenance, and recovery of identity are all difficult, expensive, and error prone, especially for small objects like CubeSats. Attaching a beacon or license plate with a unique identification number to a space object before launch would greatly simplify the task, but radio beacons are power hungry and can cause interference. This Paper describes a new concept for a satellite license plate: the extremely low resource optical identifier. The extremely low resource optical identifier is a milliwatt-scale self-powered autonomous optical beacon that can be attached to any space object to transmit a persistent identification signal to ground stations. A system appropriate for a low-Earth-orbit CubeSat or other small space object can fit in a package with the area of a postage stamp and a few millimeters thick; and it requires no power, data, or control from the host object. The concept has been validated with ground tests, and the first flight-test unit is scheduled for launch in 2018. The unique identification number of a low-Earth-orbit satellite can be determined unambiguously in a single orbital pass over a low-cost ground station.

Spacecraft relative navigation with an omnidirectional vision sensor

Space activities are continuously at risk due to the ever-increasing amount of uncontrolled space objects, called space debris, which can collide with operational spacecraft. Therefore, space agencies have started space situational awareness (SSA) programs, which aim to provide timely and accurate data regarding the space environment and hazards to infrastructure in orbit and on the ground. An essential need for SSA is the orbit determination of as many space objects as possible, in order to predict and prevent collisions. In this work, techniques for orbit determination of objects observed on short arcs are developed. This scenario frequently occurs with space debris, which are characterised by long observational gaps due to observability constraints. The classical least squares method, used for orbit determination, is revisited by studying the effect of nonlinearities in the mapping between observations and state. Differential algebra techniques are exploited, which enable the computation of high-order Taylor expansions of the residuals with respect to the state. Approximations of differential correction methods can then be avoided and the confidence region of the solution accurately characterized with a nonlinear approach. This uncertainty region can be reduced when more observations of the same objects are acquired. Thus, a method that both recognizes associated observations and sequentially reduces the solution uncertainty when two or more sets of observations are associated is proposed. The six dimensional ($6D$) association problem is addressed as a series of $2D$ and $4D$ problems to alleviate the computational cost. In case of successful association, part of the initial uncertainty is pruned away. The least squares solution and its uncertainty region can finally be used to initialize a sequential filter. A particle filter is implemented using differential algebra, so that system and measurement equations are substituted by polynomial evaluations and the resulting computational cost of the algorithm is alleviated. A population-based stochastic optimizer, the multiobjective particle swarm optimizer, is exploited to generate particles that best represent the probability distribution over solution orbits.

Real-time dim space target detection is a significant challenge in space situation awareness. This paper proposes a single-frame space object segmentation and detection method based on deep learning. Firstly, Channel and Space Attention U-net (CSAU-Net) is presented based on space image features. We remove unnecessary feature layers and add attention modules in the traditional encoder and decoder structure to enhance feature fusion and better use original feature layers. The proposed network structure can achieve accurate segmentation of space objects with fewer data training. At the same time, we construct a space target dataset for training, which contains targets with different signal-to-noise ratios to enhance the generalization of convolutional neural networks. After obtaining the segmentation masks, a simple connected component labeling method is applied to extract the centroid of the space target. Experiments show that our approach can achieve an ideal segmentation effect when the signal-to-noise ratio of the space target in the simulated dataset is 0.3. In addition, the proposed algorithm can realize fast segmentation and achieve an accuracy of 98.5%, which is similar to the traditional multi-frame space target detection method in real space image detection.