Extremely Low Resource Optical Identifier: A License Plate for Your Satellite

An experimental low-cost ground station for the small satellite project BIRD

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.

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) 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.

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.

A pursuit-evasion (PE) orbital game approach for space situational awareness (SSA) is presented to deal with imperfect measurements and informational uncertainties. In the two-sided optimization problem, a pursuer (an observer or sensor) will use the sensor resource to minimize the uncertainty (modeled by entropy) while an evader (a space object being tracked) will maximize it by performing space maneuvers. Since the cost and opportunity cost of sensor resources, pursuer will make decisions on the when to use these resources. The proposed PE approach provides a method to solve the SSA problem, where the evader will exploit the sensing and tracking model to confuse the opponent by corrupting their tracking estimates, while the pursuer wants to efficiently decrease the tracking uncertainties. A numerical simulation scenario with one space based space surveillance (SBSS) satellite as a pursuer and one geosynchronous (GEO) satellite as an evader is simulated to demonstrate the PE orbital game approach. The GEO applies the continuous low-thrust such as the Ion thrust in maneuvers. An add-on module is developed for SGP4/SDP4 algorithms to propagate the satellites with maneuvers. The GEO maneuvering strategies and on-off measurement controls for SBSS are obtained from the Nash equilibrium of the PE game.

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.

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 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.

Bidirectional reflection distribution function (BRDF) is broadly used in many fields, such as physics, remote sensing, computer graphics and scattering in optical band and microwave band. The BRDF measurement instrument used to obtain raw measurement data is presented firstly. The BRDF data of two typical space object’s surface materials (yellow and silver foil) are measured. Measurement results show that the scattering angle corresponding to the maximum value of BRDF curve is generally in the specular reflection direction; the other BRDF values decrease quickly as scattering angle deviate from the specular reflection direction. The optimum parameter of the five-parameter BRDF model are retrieved based on simulated annealing algorithm and the root mean square error is less than 6%. The feasibility and reliability of the modeling are verifed by comparing the data calculated by the five-parametric model and the data measured originally. The measuring and modeling results could provide reference for space object detection and identification.

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.

A challenging task in space situational awareness (SSA) is to track multiple space objects for the maintenance of a space object catalog (SOC). The Bayesian multitarget tracking filter addresses this issue by associating measurements to initially known or newly detected tracks and simultaneously estimating the time-varying number of targets and their orbital states. Recently, methods based on the random finite set (RFS) [1] approach have provided a general systematic treatment of multitarget systems by modeling the multitarget states as finite set valued random variables, which allow the derivation of multitarget filters based on Bayess theorem. The newly developed labeled RFS filters, including the labeled multi-Bernoulli (LMB) filter [2] and the δ-generalized LMB (δ-GLMB) filter [3], are able to maintain target identity using a labeled RFS. The LMB filter used in this study is an efficient approximation of the δ-GLMB filter, which has been used in several SSA applications [46].

There is increasing interest in the adoption of distributed, multidomain space situational awareness (SSA) sensor architectures as a means of improving both mission performance and resilience while decreasing total cost. One promising concept calls for augmenting legacy terrestrial architectures with opportunistic sensors hosted on government and commercial spacecraft. This research explores this concept by simulating the observability of resident space objects (RSOs) in medium Earth orbit (MEO) and geosynchronous orbit (GEO) by 3300 candidate sensor network architectures. Over a simulated 48 h period, a network composed of 4 terrestrial telescopes and 10 hosted payloads in low Earth orbit revisited each of over 1200 MEO and GEO RSOs in under 8.5 h. This network also proved to be highly resilient, maintaining this revisit rate in an environment in which all terrestrial telescopes were inoperable. In this maximally degraded environment, over 600 space-based sensor networks were still capable of revisiting each RSO in under 24 h. Results of this research suggest that space-based opportunistic hosted payloads stand to concurrently improve the performance and resilience of existing SSA sensor networks used for deep space object catalog maintenance.