Simulation of resident space objects detection from space-based optical imaging

Effects of phase angle and sensor properties on on-orbit debris detection using commercial star trackers

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.

As the number of resident space objects (RSOs) orbiting Earth increases, the risk of collision increases, and mitigating this risk requires the detection, identification, characterization, and tracking of as many RSOs as possible in view at any given time, an area of research referred to as Space Situational Awareness (SSA). In order to develop algorithms for RSO detection and characterization, starfield images containing RSOs are needed. Such images can be obtained from star trackers, which have traditionally been used for attitude determination. Despite their low resolution, star tracker images have the potential to be useful for SSA. Using star trackers in this dual-purpose manner offers the benefit of leveraging existing star tracker technology already in orbit, eliminating the need for new and costly equipment to be launched into space. In August 2022, we launched a CubeSat-class payload, Resident Space Object Near-space Astrometric Research (RSONAR), on a stratospheric balloon. The primary objective of the payload was to demonstrate a dual-purpose star tracker for imaging and analyzing RSOs from a space-like environment, aiding in the field of SSA. Building on the experience and lessons learned from the 2022 campaign, we developed a next-generation dual-purpose camera in a 4U-inspired CubeSat platform, named RSONAR II. This payload was successfully launched in August 2023. With the RSONAR II payload, we developed a real-time, multi-purpose imaging system with two main cameras of varying cost that can adjust imaging parameters in real-time to evaluate the effectiveness of each configuration for RSO imaging. We also performed onboard RSO detection and attitude determination to verify the performance of our algorithms. Additionally, we implemented a downlink capability to verify payload performance during flight. To add a wider variety of images for testing our algorithms, we altered the resolution of one of the cameras throughout the mission. In this paper, we demonstrate a dual-purpose star tracker system for future SSA missions and compare two different sensor options for RSO imaging.

We report on the proton radiation effects on a 1k x 1k e2v EMCCD utilized in the Nuvu EM N2 1024 camera. Radiation testing was performed at the TRIUMF Proton Irradiation Facility in Canada, where the e2v CCD201-20 EMCCD received a 105 MeV proton fluence up to 5.2x109 protons/cm2, emulating a 1 year’s radiation dose of solar protons in low earth orbit with nominal shielding that would be expected from a small or microsatellite. The primary space-based application is for Space Situational Awareness (SSA), where a small telescope images faint orbiting Resident Space Objects (RSOs) on the EMCCD, resulting in faint streaks at the photon level of signal in the images. Of particular concern is the effect of proton radiation on low level CTE, where very low level signals could be severely impaired if not lost. Although other groups have reported on the characteristics of irradiated EMCCDs, their CTE results are not portable to this application. To understand the real impact of proton irradiation the device must be tested under realistic operating conditions with representative backgrounds, clock periods, and signal levels. Testing was performed both in the laboratory and under a night sky on the ground in order to emulate a complex star background environment containing RSOs. The degradation is presented and mitigation techniques are proposed. As compared to conventional CCDs, the EMCCD with high gain allows faint and moving RSOs to be detected with a relatively small telescope aperture, at improved signal to noise ratio at high frame rates. This allows the satellite platform to take sharp images immediately upon slewing to the target without the need for complex and relatively slow attitude stabilization systems.

Space situational awareness (SSA) is becoming increasingly challenging with the proliferation of resident space objects (RSOs), ranging from CubeSats to mega-constellations. Sensors within the United States Space Surveillance Network are tasked to repeatedly detect, characterize, and track these RSOs to retain custody and estimate their attitude. The majority of these sensors consist of ground-based sensors with a narrow field of view and must be slew at a finite rate from one RSO to another during observations. This results in a complex combinatorial problem that poses a major obstacle to the SSA sensor tasking problem. In this work, we successfully applied deep reinforcement learning (DRL) to overcome the curse of dimensionality and optimally task a ground-based sensor. We trained several DRL agents using proximal policy optimization and population-based training in a simulated SSA environment. The DRL agents outperformed myopic policies in both objective metrics of RSOs’ state uncertainties and the number of unique RSOs observed over a 90-min observation window. The agents’ robustness to changes in RSO orbital regimes, observation window length, observer’s location, and sensor properties are also examined. The robustness of the DRL agents allows them to be applied to any arbitrary locations and scenarios.

With the rapid increase in resident space objects (RSO), there is a growing demand for their identification and characterization to advance space simulation awareness (SSA) programs. Various AI-based technologies are proposed and demonstrated around the world to effectively and efficiently identify RSOs from ground and space-based observations; however, there remains a challenge in AI training due to the lack of labeled datasets for accurate RSO detection. In this paper, we present an overview of the starfield simulator to generate a realistic representation of images from space-borne imagers. In particular, we focus on low-resolution images such as those taken with a commercial-grade star tracker that contains various RSO in starfield images. The accuracy and computational efficiency of the simulator are compared to the commercial simulator, namely STK-EOIR to demonstrate the performance of the simulator. In comparing over 1000 images from the Fast Auroral Imager (FAI) onboard CASSIOPE satellite, the current simulator generates both stars and RSOs with approximately the same accuracy (compared to the real images) as STK-EOIR and, an order of magnitude faster in computational speed by leveraging parallel processing methodologies.

Space debris is a term for all human-made objects orbiting the Earth or reentering the atmosphere. The population of space debris is continuously growing and it represents a potential issue for active satellites and spacecraft. New collisions and fragmentation could exponentially increase the amount of debris and so the level of risk represented by these objects. The principal technique used for the debris monitoring, in the Low Earth Orbit (LEO) between 200 km and 2000 km of altitude, is based on radar systems. The BIRALET system represents one of the main Italian radars involved in resident space objects observations. It is a bi-static radar, which operates in the P-band at 410–415 MHz, that uses the Sardinia Radio Telescope as receiver. In this paper, a detailed description of the new ad hoc back-end developed for the BIRALET radar, with the aim to perform slant-range and Doppler shift measurements, is presented. The new system was successfully tested in several validation measurement campaigns, the results of which are reported and discussed.

Light curves are plots of brightness measured over time. In the field of Space Situational Awareness (SSA), light curves of Resident Space Objects (RSOs) can be utilized to infer information about an RSO such as the type of object, its attitude, and its shape. Light curves of RSOs in geostationary orbit (GEO) have been a main research focus for many years due to the availability of long time series data spanning hours. Given that a large portion of RSOs are in low Earth orbit (LEO), it is of great importance to study trends in LEO light curves as well. The challenge with LEO light curves is that they tend to be short, typically no longer than a few minutes, which makes them difficult to analyze with typical time series techniques. This study presents a novel approach to observational LEO light curve classification. We extract features from light curves using a wavelet scattering transformation which is used as an input for a machine learning classifier. We performed light curve classification using both a conventional machine learning approach, namely a support vector machine (SVM), and a deep learning technique, long short-term memory (LSTM), to compare the results. LSTM outperforms SVM for LEO light curve classification with a 92% accuracy. This proves the viability of RSO classification by object type and spin rate from real LEO light curves.

A multiple model estimation scheme is proposed to enhance the robustness of a resident space object (RSO) tracker subject to its maneuverability uncertainties (unplanned or unknown jet firing activities) and other system variations. The concept is based on the Interacting Multiple Model (IMM) estimation scheme. Within the IMM framework, two Extended Kalman Filter (EKF) models: (i) a 6 State (Position and Velocity of a constant orbiting RSO) EKF and (ii) a 9 state (Position, Velocity, and Acceleration of a maneuvering RSO) EKF are designed and implemented to achieve RSO maneuvering detection and enhanced tracking accuracy. The IMM estimation scheme is capable of providing enhanced state vector estimation accuracy and consistent prediction of the RSO maneuvering status, thus offering an attractive design feature for future Space Situational Awareness (SSA) missions. The design concept is illustrated using the Matlab/Based Simulation testing environment.

A multiple model estimation scheme is proposed to enhance the robustness of a resident space object (RSO) tracker subject to its maneuverability uncertainties (unplanned or unknown jet firing activities) and other system variations. The concept is based on the Interacting Multiple Model (IMM) estimation scheme. Within the IMM framework, two Extended Kalman Filter (EKF) models: (i) a 6 State (Position and Velocity of a constant orbiting RSO) EKF and (ii) a 9 state (Position, Velocity, and Acceleration of a maneuvering RSO) EKF are designed and implemented to achieve RSO maneuvering detection and enhanced tracking accuracy. The IMM estimation scheme is capable of providing enhanced state vector estimation accuracy and consistent prediction of the RSO maneuvering status, thus offering an attractive design feature for future Space Situational Awareness (SSA) missions. The design concept is illustrated using the Matlab/Based Simulation testing environment.

With the increase in the number of objects orbiting Earth, Space Situational Awareness (SSA) has becoming an important area of research in the space sector. Currently most sensors that contribute to SSA are large dedicated optical or radar stations, such as space fence (Pechkis, et al., 2015). With the increase in low resolution sensors in LEO there is a growing potential to utilize these to augment current SSA efforts. Star trackers are readily available and used in space for attitude determination, with recent work performed to demonstrate the benefit of using spaced-based optical measurements for Resident Space Object (RSO) detection. In this paper, we describe the interpretation of space-based measurement for light curve of an RSOs to estimate the RSOs shape, attitude and optical properties. In this model, two Bidirectional Reflectance Distribution Functions (BDRF’s) are compared, namely a defined facet model and an anthropic Phong model. From the initial results an RSOs shape, attitude, optical properties can be estimated with basic a-priory information on the shape of the RSO with both models.

The determination of the full population of resident space objects (RSOs) in low Earth orbit (LEO) is a key issue in the field of space situational awareness that will only increase in importance in the coming years. We endeavour to describe a novel method of inferring the population of RSOs as a function of orbital height and inclination for a range of magnitudes. The method described uses observations of an orbit of known height and inclination to detect RSOs on neighbouring orbits. These neighbouring orbit targets move slowly relative to our tracked orbit, and are thus detectable down to faint magnitudes. We conduct simulations to show that, by observing multiple passes of a known orbit, we can infer the population of RSOs within a defined region of orbital parameter space. Observing a range of orbits from different orbital sites will allow for the inference of a population of LEO RSOs as a function of their orbital parameters and object magnitude.

In recent years, there has been a significant increase in satellite launches, resulting in a proliferation of satellites in our near-Earth space environment. This surge has led to a multitude of resident space objects (RSOs). Thus, detecting RSOs is a crucial element of monitoring these objects and plays an important role in preventing collisions between them. Optical images captured from spacecraft and with ground-based telescopes provide valuable information for RSO detection and identification, thereby enhancing space situational awareness (SSA). However, datasets are not publicly available due to their sensitive nature. This scarcity of data has hindered the development of detection algorithms. In this paper, we present annotated RSO images, which constitute an internally curated dataset obtained from a low-resolution wide-field-of-view imager on a stratospheric balloon. In addition, we examine several frame differencing techniques, namely, adjacent frame differencing, median frame differencing, proximity filtering and tracking, and a streak detection method. These algorithms were applied to annotated images to detect RSOs. The proposed algorithms achieved a competitive degree of success with precision scores of 73%, 95%, 95%, and 100% and F1 scores of 68%, 77%, 82%, and 79%.

Improving space situational awareness (SSA) remains one of the Department of Defense’s (DoD) top priorities. Current research has shown that the modeling of geosynchronous orbit (GEO) SSA architectures can help identify optimal combinations of ground- and space-based sensors. This paper extends previous research by expanding design boundaries and refining the methodology. A multi-objective genetic algorithm was used to examine this increased trade-space containing 10 22 possible design combinations. The results of the optimizer clearly favor 1.0 m aperture ground telescopes combined with 0.15 m aperture sensors in a 12-satellite geosynchronous polar orbit (GPO) constellation. The GPO regime offers increased access to GEO resident space objects (RSO) since other orbits are restricted by a 40° solar exclusion angle. When performance is held constant, a GPO satellite constellation offers a 22.4% reduction in total system cost when compared to Sun synchronous orbit (SSO), equatorial low earth orbit (LEO), and near-GEO constellations. Parallel high-performance computing provides the possibility of solving an entirely new class of complex problems of interest to the DoD. The results of this research can educate national policy makers on the benefits of proposed upgrades to current and future SSA systems.

The Space Surveillance Catalog is a database of all Resident Space Objects (RSOs) on earth orbit. It is expected to grow in the future as more RSOs accumulate on orbit. Potentially still more dramatic growth could follow the deployment of the Space Based Infrared System Low Earth Orbit Component (SBTRS Low). SBIRS Low, currently about to enter development, offers the potential to detect and acquire much smaller debris RSOs than can be seen by the current ground-based Space Surveillance Network (SSN). SBIRS Low will host multicolor infrared/visible sensors on each satellite in a proliferated constellation on low earth orbit, and if appropriately tasked, these sensors could provide significant space surveillance capability. Catalog growth during SBIRS Low deployment was analyzed using a highly aggregated code that numerically integrates the Markov equations governing the state transitions of RSOs from uncataloged to cataloged, and back again. It was assumed that all newly observed debris RSOs will be detected as by-products of routine Catalog maintenance, not including any post breakup searches, and if sufficient sensor resources are available, be acquired into the Catalog. Debris over the entire low to high altitude regime were considered.