Resident Space Objects Research Articles

A Data-driven Method for Realistic Covariance Prediction of Space Object with Sparse Tracking Data

Covariance of the orbital state of a resident space object (RSO) is a necessary requirement for various space situational awareness tasks, like the space collision warning. It describes an accuracy envelope of the RSO's location. However, in current space surveillance, the tracking data of an individual RSO is often found insufficiently accurate and sparsely distributed, making the predicted covariance (PC) derived from the tracking data and classical orbit dynamic system usually unrealistic in describing the error characterization of orbit predictions. Given the fact that the tracking data of an RSO from a single station or a fixed network share a similar temporal and spatial distribution, the evolution of PC could share a hidden relationship with that data distribution. This study proposes a novel method to generate accurate PC by combining the classical covariance propagation method and the data-driven approach. Two popular machine learning algorithms are applied to model the inconsistency between the orbit prediction error and the PC from historical observations, and then this inconsistency model is used for the future PC. Experimental results with the Swarm constellation satellites demonstrate that the trained Random Forest models can capture more than 95% of the underlying inconsistency in a tracking scenario of sparse observations. More importantly, the trained models show great generalization capability in correcting the PC of future epochs and other RSOs with similar orbit characteristics and observation conditions. Besides, a deep analysis of generalization performance is carried out to describe the temporal and spatial similarities of two data sets, in which the Jaccard similarity is used. It demonstrates that the higher the Jaccard similarity is, the better the generalization performance will be, which may be used as a guide to whether to apply the trained models of a satellite to other satellites. Further, the generalization performance is also evaluated by the classical Cramer von Misses test, which also shows that trained models have encouraging generalization performance.

GEO spacecraft maneuver detection based on causal inference

Geosynchronous (GEO) spacecraft maneuver detection is deemed crucial for space domain awareness (SDA) as it helps to maintain catalogs of high value resident space objects (RSOs). However, several difficulties in resolving maneuver time and requirements for historical data quality still exist for the current research. This paper proposes a method based on causal inference for accurate and timely detection of the maneuver using real-time observational data from optical sensors. Firstly, the residual information of time series observations is obtained through counterfactual reasoning. Secondly, according to the Gaussianity of the optical sensor observation noise, a structural causal equation (SCE) of the residuals of time series observations is derived. Lastly, the maneuver is abstracted as a soft intervention, and the minimum average treatment effect (MATE) is proposed to estimate the effect of the maneuver and used as a feature parameter to identify the GEO spacecraft maneuver. After experimental analysis, the detection rate and detection timeliness of the proposed method reach more than 90%, a significant improvement compared with the conventional χ2, skewness, and kurtosis test methods.

CMOS-based observation of Resident Space Objects using short-exposure stacking mode

To meet the growing demand for the surveillance of resident space objects, the Shanghai Astronomical Observatory has developed a new observation method for monitoring resident space objects using a high frame rate astronomical CMOS camera, called the short-exposure stacking mode. This method can be used to observe objects of various orbit types with high observation accuracy. This paper describes the implementation of this new method of RSO observation, and the observation experiment of one MEO satellite and two GEO satellites using this method with a 40 cm telescope, the observation system has a 0.5°×0.5° field of view. The observation results show that the accuracy of the sensor calibration is 0.3-0.4 arc-second. Compared with the external precision ephemeris, the standard deviation of the residual differences is better than 0.5 arc-second, which means that the precise orbit of space objects can be obtained with enough observation. And compared with the method that uses drift scan CCD, the observation time efficiency of the short-exposure stacking mode is better.

Relative Navigation Strategy About Unknown and Uncooperative Targets

In recent years, space debris has become a threat for satellites operating in low Earth orbit. Even by applying mitigation guidelines, their number will still increase over the course of the century. As a consequence, active debris removal missions and on-orbit servicing missions have gained momentum at both academic and industrial level. The crucial step in both scenarios is the capability of navigating in the neighborhood of a target resident space object. This problem has been tackled many times in literature with varying level of cooperativeness of the target required. While several techniques are available when the target is cooperative or its shape is known, no approach is mature enough to deal with uncooperative and unknown targets. This paper proposes a hybrid method to tackle this issue called Coarse Model-Based Relative Navigation (CoMBiNa). The main idea of this algorithm is to split the mission into two phases. During the first phase, the algorithm constructs a coarse model of the target. In the second phase, this coarse model is used as a reference for a relative navigation technique, effectively shifting the focus toward state and inertia estimation. In addition, this paper proposes a strategy to leverage the structure of the selected navigation method to detect and reject outliers. To conclude, CoMBiNa is tested on a simulated environment to highlight its benefits and its shortcomings, while also assessing its applicability on a limited-resource single-board computer.

Star map matching method for optical circular rotation imaging based on graph neural networks.

This paper focuses on a dynamic star image acquisition and matching method for space situational awareness, which can quickly search for widely distributed resident space objects. First, the optical circular rotation imaging method performed by a single space camera is proposed to obtain a series of star images. And then, the image matching method based on graph neural networks is proposed for generating a wide observation star image. Experiment results show that compared with baseline matching algorithms, the matching accuracy and matching precision of the proposed algorithm are improved significantly.

Passive Electro-Optical Tracking of Resident Space Objects for Distributed Satellite Systems Autonomous Navigation

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods.

A Staring Tracking Measurement Method of Resident Space Objects Based on the Star Tacker

Measuring resident space objects (RSOs) by star trackers has become a research hotspot in space situational awareness. However, the arc length measured by star trackers is too short to complete the high-precision orbit determination of the RSO. In this paper, a staring–tracking measurement (STM) method is proposed to obtain a sufficiently long arc. Firstly, the three-axis attitude calculation model of the satellite is established for tracking RSOs during the staring process. Secondly, an observation method of the RSO location is proposed based on the principle of the angular distance invariance and the imaging mapping function of the star tracker. Finally, the numerical simulations and the ground experiment were conducted to verify the proposed algorithm. Simulation results show that the acquisition time of the measurable arcs is longer than 6 min, and the accuracy of the declination and the ascension can meet the standard for RSO orbit determination. The physical experiment shows that the acquisition time is consistent with that in the simulation. Our work provides a new idea for the realization of high-precision space-based optical measurement.

Machine learning-based approach for ballistic coefficient estimation of resident space objects in LEO

The increasing number of Resident Space Objects poses a serious threat to the safe operation of satellites. Alongside with mitigation policies, it is fundamental to predict the trajectories of such objects which requires the accurate estimation of the physical characteristics that influence their orbits. An important role is played by the area-to-mass ratio, i.e., the ratio between the area exposed to the atmosphere/the Sun and the mass of the space object for the LEO/MEO and GEO region. Current literature proposes several approaches for the estimation of the area-to-mass ratio, ranging from semi-analytical to numerical methods. As regards the latter category, recent studies have focused on classification or regression algorithms for specific types of orbits (e.g., sun-synchronous, geostationary).In this context, this paper proposes a machine learning-based regression approach for the estimation of the ballistic coefficient (i.e., the product between the drag coefficient and the area-to-mass ratio) in Low Earth Orbit, covering a wide set of orbital parameters. Using a synthetic space catalogue, the performance of different types of machine learning techniques is evaluated and compared. A sensitivity analysis is carried out to analyse the effect on performance of the number of trainings, the propagation time, the number of objects of the training data set, and the frequency of the measurements. The applicability of the presented approach is tested and discussed using both real (i.e., based on publicly available catalogues of Two-Line Elements) and synthetic datasets.

Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation

The present work focuses on the investigation of an artificial intelligence (AI) algorithm for brightest objects segmentation in night sky images’ field of view (FOV). This task is mandatory for many applications that want to focus on the brightest objects in an optical sensor image with a particular shape: point-like or streak. The algorithm is developed as a dedicated application for star sensors both for attitude determination (AD) and onboard space surveillance and tracking (SST) tasks. Indeed, in the former, the brightest objects of most concern are stars, while in the latter they are resident space objects (RSOs). Focusing attention on these shapes, an AI-based segmentation approach can be investigated. This will be carried out by designing, developing and testing a convolutional neural network (CNN)-based algorithm. In particular, a U-Net will be used to tackle this problem. A dataset for the design process of the algorithm, network training and tests is created using both real and simulated images. In the end, comparison with traditional segmentation algorithms will be performed, and results will be presented and discussed together with the proposal of an electro-optical payload for a small satellite for an in-orbit validation (IOV) mission.

The adaptive Gaussian mixtures unscented Kalman filter for attitude determination using light curves

The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaussian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kalman Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non-linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can converge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state.

Space-Based Sensor Tasking Using Deep Reinforcement Learning

To maintain a robust catalog of resident space objects (RSOs), space situational awareness (SSA) mission operators depend on ground- and space-based sensors to repeatedly detect, characterize, and track objects in orbit. Although some space sensors are capable of monitoring large swaths of the sky with wide fields of view (FOVs), others—such as maneuverable optical telescopes, narrow-band imaging radars, or satellite laser-ranging systems—are restricted to relatively narrow FOVs and must slew at a finite rate from object to object during observation. Since there are many objects that a narrow FOV sensor could choose to observe within its field of regard (FOR), it must schedule its pointing direction and duration using some algorithm. This combinatorial optimization problem is known as the sensor-tasking problem. In this paper, we developed a deep reinforcement learning agent to task a space-based narrow-FOV sensor in low Earth orbit (LEO) using the proximal policy optimization algorithm. The sensor’s performance—both as a singular sensor acting alone, but also as a complement to a network of taskable, narrow-FOV ground-based sensors—is compared to the greedy scheduler across several figures of merit, including the cumulative number of RSOs observed and the mean trace of the covariance matrix of all of the observable objects in the scenario. The results of several simulations are presented and discussed. Additionally, the results from an LEO SSA sensor in different orbits are evaluated and discussed, as well as various combinations of space-based sensors.

Optimal Tasking of Ground-Based Sensors for Space Situational Awareness Using Deep Reinforcement Learning.

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.

Resident Space Object Characterization Using Polarized Light Curves

Light curves, or the time-history of photometric brightness, have previously been demonstrated to allow for estimation of a space object’s attitude, shape, and surface parameters. However, these methods are subject to many limitations, including the need for accurate initial estimates to initialize filters, and the inability to regularly estimate attitude and surface parameters simultaneously due to information dilution, or the increase in uncertainty of each state when more states are simultaneously estimated. Monochromatic, unpolarized light curves can also only allow for surface reflectance parameter determination, and not determination of the materials themselves. This work develops a polarized bidirectional reflectance distribution function, and demonstrates that polarized measurements increase the uniqueness of light-curve attitude solutions to allow larger tolerance to initial error while also providing surface material estimation. Polarization of reflected light can also be measured more accurately than the intensity of reflected light through the atmosphere, thereby increasing the attitude observability from the light curves. Observability of attitude and surface materials is assessed. An unscented multiple-model adaptive estimation filter is implemented to estimate attitude, angular rate, surface material, surface roughness, and size of the space object.

Improving Orbital Uncertainty Realism Through Covariance Determination in GEO

The reliability of the uncertainty characterization, also known as uncertainty realism, is of the uttermost importance for Space Situational Awareness (SSA) services. Among the different sources of uncertainty related to the orbits of Resident Space Objects (RSOs), the uncertainty of dynamic models is one of the most relevant ones, although it is not always included in orbit determination processes. A classical approach to account for these sources of uncertainty is the consider parameters theory, which consists in including parameters in the underlying dynamical models whose variance aims to represent the uncertainty of the system. However, realistic variances of these consider parameters are not known a-priori. This work presents a method to infer the variance of the consider parameters, based on the distribution of the Mahalanobis distance of the orbital differences between predicted and estimated orbits, which theoretically shall follow a chi ^2 distribution under Gaussian assumption. This paper presents results in a simulated scenario focusing on Geostationary (GEO) regimes. The effectiveness and traceability of the uncertainty sources is assessed via covariance realism metrics.

Track detection of high-velocity resident space objects in Low Earth Orbit

The world’s economy has become heavily dependent on the services provided by satellites. With the exponential increase in satellite launches, the population of defunct or inactive hardware in space has grown substantially. This is especially true in sensitive orbits such as the Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. These objects, collectively known as orbital debris, can reach speeds of up to 28 000km h−1 in LEO. At these orbital speeds, even the smallest of objects can pose a considerable threat to operational satellites or astronauts. This makes the monitoring, and detection, of these objects of the utmost importance. This work describes the latest detection strategy used in one of Europe’s largest Space Situational Awareness (SSA) installation; the BIstatic RAdar for LEo Survey (BIRALES) space debris radar. We present a novel bottom-up approach that makes use of single-linkage clustering to identify faint radar streaks in spectrogram data. Tests on synthetic data have shown that the detection strategy presented in this study obtains a higher detection rate when it is compared against existing methods. Unlike other approaches, this detection strategy, using the Multi-beam streak detection strategy (MSDS) algorithm, was still able to recall 90% of the track information at an Signal-to-Noise Ratio (SNR) of 2dB.

Resident space object (RSO) attitude and optical property estimation from space-based light curves

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.

Satellite maneuver detection and estimation with radar survey observations

The space environment is rapidly getting congested, inducing a growth of the risk of collision between Resident Space Objects (RSOs). The validity of collision risk assessment depends on the quality of the catalogs of RSOs, which shall be as accurate and up-to-date as possible. Maneuvers of operational satellites represent a problem, because, if not correctly detected and estimated, they could lead to catalog degradation or pollution. In this paper, a novel approach to tackle the maneuver detection and estimation problem is presented. The methodology, which is part of the association problem between tracks and objects, is intended to be included in real-time cataloging systems to increase their flexibility. This paper is a continuation, extension and improvement of a former work, where maneuver estimation was carried out with optical observations. The estimation algorithm is extended to radar observations with the inclusion of a new dynamical model. The joint detection and estimation scheme is tested in a simulated maintenance chain with tracks and orbits from a single satellite. In this scenario, the output of the maneuver estimation is then used as an a-priori guess in high-fidelity orbit determination, which is refined and used to compute a post-maneuver orbit. The results of the application of the detection and estimation algorithm within the simulated chain are presented, highlighting the advantages and validating the methodology, providing a basis for a wider multi-target multi-sensor association framework, with the final goal of solving the association problem with data from survey activities.

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor.

A catalogue of over 22,000 objects in Earth’s orbit is currently maintained, and that number is expected to double within the next decade. Novel data collection regimes are needed to scale our ability to detect, track, classify and characterize resident space objects in a crowded low Earth orbit. This research presents RSOnet, an image-processing framework for space domain awareness using star trackers. Star trackers are cost-effective, flight proven, and require basic image processing to be used as an attitude-determination sensor. RSOnet is designed to augment the capabilities of a star tracker by becoming an opportunistic space-surveillance sensor. Our research demonstrates that star trackers are a feasible source for RSO detections in LEO by demonstrating the performance of RSOnet on real detections from a star-tracker-like imager in space. RSOnet convolutional-neural-network model architecture, graph-based multi-object classifier and characterization results are described in this paper.

Ground-based laser momentum transfer concept for debris collision avoidance

Satellite laser ranging (SLR) is a well-established technology within the scientific community used since the early 1960s to precisely measure distances. The technology evolved in support of the geodetic research striving for high accuracy measurements which nowadays are achieved by means of high repetition, low energy pulse lasers used in combination with satellites equipped with retroreflectors. The achieved accuracy allowed not only for quality improvement of orbit determination products but also remote estimation of the attitude of the observed target.The SLR technique constitutes a highly accurate, relatively cheap alternative to radars for the tracking of orbiting targets. In the last decade, the successful tracking of resident space objects, not equipped with retroreflectors, made SLR a fundamental and appealing technique also in the space debris domain.In this study, we will introduce a possible step forward - thanks to the availability of commercial high power (> 10 kW) continuous wave (CW) lasers - which consists in the setup of a network of ground stations able to efficiently contribute to space debris collision avoidance manoeuvres in the low Earth orbit (LEO).This paper will summarize the achievements of a conceptual study on ground-based laser momentum transfer to LEO space debris performed by a consortium under the guidance of the German Aerospace Centre (DLR) funded by the European Space Agency (ESA) in the frame of the ESA Space Safety Programme.The study was carried out approaching the problem from an astrodynamics, physical, technological and legal point of view. The required tracking precision and the fundamental physics of the laser momentum transfer (LMT) were studied to evaluate the achievable thrust on LEO debris objects with commercially available components. An astrodynamics analysis was carried out to assess the efficiency of the imposed thrust and the consequences on the probability of collision in LEO. In the paper we will report the outcomes of the study which allowed us to define: the requirements of a laser tracking and momentum transfer (LTMT) station, the minimum size of an LTMT network for LEO collision avoidance operation, the current technological challenges, and gaps to be filled before its implementation.

Track-to-track association methodology for operational surveillance scenarios with radar observations

This paper proposes a novel track-to-track association methodology able to detect and catalogue resident space objects (RSOs) from associations of uncorrelated tracks (UCTs) obtained by radar survey sensors. It is a multi-target multi-sensor algorithm approach able to associate data from surveillance sensors to detect and catalogue objects. The association methodology contains a series of steps, each of which reduces the complexity of the combinational problem. The main focus are real operational environments, in which brute-force approaches are computationally unaffordable. The hypotheses are scored in the measurement space by evaluating a figure of merit based on the residuals of the observations. This allows us to filter out most of the false hypotheses that would be present in brute-force approaches, as well as to distinguish between true and false hypotheses. The suitability of the proposed track-to-track association has been assessed with a simulated scenario representative of a real operational environment, corresponding to 2 weeks of radar survey data obtained by a single survey radar. The distribution and evolution of the hypotheses along the association process is analysed and typical association performance metrics are included. Most of the RSOs are detected and catalogued and only one false positive is obtained. Besides, the rate of false positives is kept low, most of them corresponding to particular cases or objects with high eccentricity or limited observability.