Resident Space Objects Research Articles

Deep Neural Network Closed-loop with Raw Data for Optical Resident Space Object Detection

Optical survey is an important means for observing resident space objects and space situational awareness. With the application of astronomical techniques and reduction method, wide field of view telescopes have made significant contributions in discovering and identifying resident space objects. However, with the development of modern optical and electronic technology, the detection limit of instruments and infrastructure has been greatly extended, leading to an extensive number of raw images and many more sources in these images. Challenges arise when reducing these data in terms of traditional measurement and calibration. Based on the amount of data, it is particularly feasible and reliable to apply machine learning algorithms. Here an end-to-end deep learning framework is developed, it is trained with a priori information on raw detections and the automatic detection task is performed on the new data acquired. The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope. It is demonstrated that our framework can achieve high performance compared with the traditional method, and with data fusion, the efficiency of the system can be improved without changing hardware or deploying new devices. The technique deserves a wider application in many fields of observational astronomy.

Design and tests of filtering actions for an AI-based RSOs detection and tracking algorithm

Space debris represent a huge threat for the actual and future space traffic. The continuous monitoring of the space environment around the Earth and the build-up, maintenance of Resident Space Objects (RSOs) catalogue are essential to enhance the space segment safety. In the last few years, trend towards Space Based Space Surveillance (SBSS) missions was relevant. In particular, the monitoring of space debris through on-board Star Trackers (STs) coupled with Artificial Intelligence (AI) based algorithms is being studied, modeling RSOs as clear streaks on a dark background. This work presents the advances in the development of an AI-based algorithm for streaks-like RSOs Detection & Tracking for on-board optical sensors applications like STs. The initial design of the RSOs monitoring algorithm (Mastrofini et al., 2022) is extended and tested for what concerns the tracking part with new filtering actions. The goal is the improvement of the tracking outputs quality and reliability when multiple RSOs passages occur, with different configurations. Tests both on real and simulated images from STs are shown and discussed.

Attitude motion classification of resident space objects using light curve spectral analysis

The characterization of Earth-orbiting objects in terms of attitude motion, material composition, and shape is crucial for Space Domain Awareness. In this respect, light curves, i.e., graphs showing the brightness variation of space objects over a specific time interval, can be used to infer both their dynamical and physical properties. However, such light curve inversion problem is challenging due to measurement noise, data gaps, and the intrinsic difficulty in separating the multiple effects impacting on the object’s brightness. Existing light curve inversion methods rely on estimation filters exploiting data fusion, Machine Learning approaches, or are based on the analysis of the light-curve frequency spectra. Many of these approaches make assumptions about a-priori knowledge of target object’s characteristics, such as shape, size, surface reflective properties, and/or observation parameters, e.g., target’s orbit and observation geometry. In this framework, this paper presents an innovative architecture for the classification of the attitude motion of space objects using only photometric light curve data, not requiring any a-priori assumption or knowledge. The proposed architecture exploits the Lomb-Scargle Periodogram algorithm to retrieve a frequency spectrum from light curve measurements, and then performs the classification by analyzing the spectrum characteristics testing different candidate rotation periods through the Phase Dispersion Minimization method. The proposed classification algorithm is first tested using a light curve simulator, in which any complex geometric model and different surface properties can be generated. Finally, the method is applied to real light curves retrieved from a publicly available database to assess the classification performance.

Too-Short-Arc Association and Clustering Method for Space-Based Optical Observations

Space-based optical observation is assuming an increasingly significant role in space surveillance and tracking. However, it often yields tracks that are too short, which are known as too short arcs (TSAs). A single TSA lacks sufficient information for reliable initial orbit determination of a resident space object. Therefore, it is typically necessary to perform tracklet association during catalog building and maintenance. This study presents a novel tracklet association method that improves upon existing techniques by combining admissible regions and nonlinear orbital uncertainty propagation. Proceeding by constructing TSA association matrices and TSA clustering matrices, the bond energy algorithm, traditionally employed in the design of distributed database systems, is applied to cluster TSAs based on the association results between two arbitrary TSAs. Furthermore, the proposed splitting algorithm reduces the computational load associated with clustering multiple tracklets and effectively handles erroneous association results. To assess the effectiveness of this approach, it was tested in a space-based optical-survey scenario, accounting for practical observation uncertainties. The simulation results demonstrate that the method achieves a high level of accuracy in both TSA association and clustering.

A Robust Star Identification Algorithm for Resident Space Object Surveillance

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance.

The Resident Space Object Detection Method Based on the Connection between the Fourier Domain Image of the Video Data Difference Frame and the Orbital Velocity Projection

The Resident Space Object Detection Method Based on the Connection between the Fourier Domain Image of the Video Data Difference Frame and the Orbital Velocity Projection

Predicting RSO populations using a neighbouring orbits technique

Abstract 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.

Safety considerations for large constellations of satellites

The deployment of constellations of satellites within low Earth orbit (LEO) has implications for space operations and for the broader space environment. A large active satellite population will experience high numbers of conjunctions with other resident space objects (RSOs). Even if only a small proportion are high-probability events, the substantial number of conjunctions will still lead to many potentially high-risk encounters with other RSOs and a correspondingly high burden for their operators to mitigate them via maneuvers. This burden is exacerbated if the operator adopts an approach whereby risk mitigation maneuvers are conducted at collision probability levels below the widely accepted 1E-4 (1-in-10,000). Despite these significant efforts the remaining aggregate risk may still be relatively high because of the large number of conjunctions experienced by some constellations, leading to ongoing concern over the safety of these space systems. Through an analysis of conjunction assessment data, simulations using the DAMAGE computational model, and a new mapping approach, the risks from conjunctions between large constellations and other RSOs have been investigated. The results show that some existing constellations currently face more than a 10 % annual collision probability even after accounting for their robust risk mitigation approaches, with implications for the safety and long-term sustainability of large constellations and the broader LEO environment. Overall, the work emphasizes the need for new research and guidance on this aspect of space operations.

In-situ observations of resident space objects with the CHEOPS space telescope

The CHaracterising ExOPlanet Satellite (CHEOPS) is a partnership between the European Space Agency and Switzerland with important contributions by 10 additional ESA member States. It is the first S-class mission in the ESA Science Programme. CHEOPS has been flying on a Sun-synchronous low Earth orbit since December 2019, collecting millions of short-exposure images in the visible domain to study exoplanet properties.A small yet increasing fraction of CHEOPS images show linear trails caused by resident space objects crossing the instrument field of view. CHEOPS’ orbit is indeed particularly favourable to serendipitously detect objects in its vicinity as the spacecraft rarely enters the Earth's shadow, sits at an altitude of 700 km, and observes with moderate phase angles relative to the Sun. This observing configuration is quite powerful, and it is complementary to optical observations from the ground.To characterize the population of satellites and orbital debris observed by CHEOPS, all and every science images acquired over the past 3 years have been scanned with a Hough transform algorithm to identify the characteristic linear features that these objects cause on the images. Thousands of trails have been detected. This statistically significant sample shows interesting trends and features such as an increased occurrence rate over the past years as well as the fingerprint of the Starlink constellation. The cross-matching of individual trails with catalogued objects is underway as we aim to measure their distance at the time of observation and deduce the apparent magnitude of the detected objects.As space agencies and private companies are developing new space-based surveillance and tracking activities to catalogue and characterize the distribution of small debris, the CHEOPS experience is timely and relevant. With the first CHEOPS mission extension currently running until the end of 2026, and a possible second extension until the end of 2029, the longer time coverage will make our dataset even more valuable to the community, especially for characterizing objects with recurrent crossings.

Multi-frame temporal dense nested attention method for detecting GEO objects

The geostationary orbit is a special and important spatial orbital resource. However, space resident objects in this orbit pose potential threats to space security, characterized as dim and small targets in imaging features. To solve the problem of detecting dim and small targets in geostationary orbit, we propose a multi-frame temporal dense nested attention method, which consists of a uniform linear motion constraint module and a dense nested attention module. The dense nested attention module adopts a multi-layer U-shaped structure and channel spatial attention mechanism, which can preserve the deep features of small targets, extract and fuse features from different scales of feature maps, and finally obtain the preliminary detection results of single-frame images. The uniform linear motion constraint module uses the prior information of the target motion in the image sequence, removes false positives and supplements false negatives, and improves the detection performance. Experiments were conducted on the SpotGEO dataset publicly released by the European Space Agency. The results of our experiments indicate that our method outperforms the state-of-the-art approach, showing a 12.83% reduction in Mean Squared Error compared to the current leading method. Our method, characterized by its minimal parameter requirement and independence from extensive dataset pre-training, is ideally suited for integration into small satellite devices, enabling efficient monitoring and mitigation of resident space objects.

Adaptive track approach for multiple sources scenarios during radar survey for space surveillance applications

The increasing population of resident space objects is currently fostering many space surveillance initiatives, which rely on the use of on-ground sensors. In particular, survey radars allow to first characterise the target orbit from a single transit, through measurements which are Doppler shift, slant range and angular profile. In this framework, the Music Approach for Track Estimate and Refinement (MATER) algorithm was developed to compute the angular track through the MUltiple SIgnal Classification (MUSIC), by solving possible ambiguities which may arise because of the receiver array geometry.This work presents the MATER extension to the case in which multiple sources are simultaneously detected by the sensor. For each detected source, the signal Direction of Arrival (DOA) is computed through MUSIC, and, if no prediction is exploited, possible ambiguous solutions arise. The computation is repeated for the entire observation, and all the estimations related to a specific target are grouped based on the angular sequence shape and the detection epochs. Finally, the possible ambiguity problem is solved, and the angular profile is obtained through a quadratic regression in time.The algorithm is numerically tested on a survey observation simulation. The detection length depends on the impinging signal intensity, and the angular accuracy is in the order of 1e-03 deg. A sensitivity analysis highlights that a transmitted power decrease shortens the detection length, with no remarkable angular accuracy deterioration. An additional simulation shows that in proximity operation monitoring MATER performance depends on the mutual geometry between target and chaser, as it may bring down the reciprocal angular distance under the resolution level. Nevertheless, it is always possible to identify the presence of both sources through the eigenvalues analysis of the signal correlation matrix. Finally, the simulation of a fragments cloud observation highlights that MATER performance depends both on the size of the observed fragment, as this is strictly linked to the signal detected by the receiver, and on the simultaneous detection of other fragments with a more effective signal.

Initial orbit determination via artificial intelligence for too-short arcs

In the framework of Space Traffic Management (STM) the growing number of space debris and the consequent increased risk of collisions have become a topic of great interest. Moreover, the difficulty of detecting and characterizing a Resident Space Object (RSO) in terms of orbital parameters makes this research field very challenging. In most cases the available observations have a very short duration and an accurate estimation of the orbit is very difficult. This paper introduces the Initial Orbit Determination via Artificial intelligence (IODA) algorithm, a reliable and accurate orbit estimator combining metaheuristic and artificial intelligence frameworks to perform precise orbit determination from Very Short Arcs (VSAs). Starting from an initial orbit estimate derived through an Artificial Bee Colony (ABC) algorithm, a Physics Informed Extreme Learning Machine is employed to increase the accuracy of the predicted orbit. Results on simulated data have shown the robustness of the algorithm to random parameters initialization and a percentage of success of 89% of retrieving the reference orbit with an error under 1 km and 0.01 km/s on the initial target’s state.

Doppler variations in radar observations of resident space objects: Likely ionospheric Pc1 plasma waves

Radars performing observations of resident space objects (RSO) measure Doppler variations in wave events of several minutes duration, with frequencies in the 0.2–1 Hz range peaking near 0.5–0.7 Hz, consistent with variations in electron density induced by waves in the intervening ionosphere. The two mid-latitude radars used were a Very High Frequency (VHF) radar operating at 55 MHz, and a High Frequency (HF) radar operating at 30 MHz, both in southern Australia.The VHF radar wave observations exibited a peak in wave occurrence in the post-dawn sector (0600–1200 local solar time). The seasonal occurrence of the waves had a strong minimum during winter compared with the other seasons. Comparison between observations in 2018/19 (near solar minimum) and 2021/22 (mid-rise to cycle 25 peak) suggests wave occurrence is anti-correlated with sunspot number and hence with EUV and ionospheric strength. Generally the waves had higher frequencies during night than day, and low sunspot number than mid solar cycle, when there was a weaker ionosphere.Given the oscillation frequency of the wave events, the most likely geophysical phenomena that is consistent with the observations are electro-magnetic ion cyclotron (EMIC) plasma waves in the Pc1 (0.2–5 Hz) frequency range. No other candidate geophysical disturbance appears to fit the wave characteristics. The majority of geomagnetic field-guided transverse Pc1 EMICWs project from the outer magnetosphere down onto the polar ionosphere, where they can convert to compressional fast-mode waves propagating parallel to the Earths surface in the ionospheric F2 layer waveguide, both equatorwards to mid-latitudes and polewards. It is likely the majority of the Doppler oscillations in the Pc1 frequency range observed by the radars at mid-latitudes are these ducted compressional waves. However, sources of transverse EMICWs from the magnetosphere onto the mid-latitude ionosphere do exist, and these may cause some of the observed oscillations. The micro-physics of these compressional waves causing Doppler oscillations in radio observations is not inconsistent with the history of ionospheric Doppler measurements and theory, although the radar trans-ionospheric radio propagation and the observed waves being higher frequency than previous studies is different.If the observed Doppler oscillations are compressional EMICW in the ionospheric waveguide then several of the statistical results can be explained. The anti-correlation of the wave occurrence with sunspot number (and resultant ionospheric strength) can be attributed to lower ionospheric attenuation at the higher latitudes, between where geomagnetically field-guided transverse EMIC waves initially enter the high-latitude ionospheric waveguide from the magnetosphere above, and their observation at mid-latitudes. The observation of higher frequency waves during low sunspot number may also be explained by a source effect in the magnetosphere that preferentially selects the higher frequency field-guided transverse EMIC waves to propagate down to the ionosphere.Comparisons will be shown with results from ground and space based magnetometers of compressional waves in the waveguide, highlighting consistent results and areas where the measurement techniques differ. Theory suggests the radar Doppler measurements are far more sensitive to variations in in-situ ionospheric electron density than magnetic field variations. This agrees with existing literature which highlights the very strong contribution from the compressional component. Theory also suggests that the Doppler sensitivity of a radar to ionospheric electron density variations is frequency dependent, with lower frequencies being more sensitive. This is borne out by the measurements, with the HF radar being more sensitive than the VHF radar.

Operational Angular Track Reconstruction in Space Surveillance Radars through an Adaptive Beamforming Approach

In the last few years, many space surveillance initiatives have started to consider the problem represented by resident space object overpopulation. In particular, the European Space Surveillance and Tracking (EUSST) consortium is in charge of providing services like collision avoidance, fragmentation analysis, and re-entry, which rely on measurements obtained through ground-based sensors. BIRALES is an Italian survey radar belonging to the EUSST framework and is capable of providing measurements including Doppler shift, slant range, and angular profile. In recent years, the Music Approach for Track Estimate and Refinement (MATER) algorithm has been developed to retrieve angular tracks through an adaptive beamforming technique, guaranteeing the generation of more accurate and robust measurements with respect to the previous static beamforming approach. This work presents the design of a new data processing chain to be used by BIRALES to compute the angular track. The signal acquired by the BIRALES receiver array is down-converted and the receiver bandwidth is split into multiple channels, in order to maximize the signal-to-noise ratio of the measurements. Then, the signal passes through a detection block, where an isolation procedure creates, for each epoch, signal correlation matrices (CMs) related to the channels involved in the detection and then processes them to isolate the data stream related to a single detected source. Consequently, for each epoch and for each detected source, just the CM featuring the largest signal contribution is kept, allowing deriving the Doppler shift measurement from the channel illumination sequence. The MATER algorithm is applied to each CM stream, first estimating the signal directions of arrival, then grouping them in the observation time window, and eventually returning the target angular track. Ambiguous estimates may be present due to the configuration of the receiver array, which cause spatial aliasing phenomena. This problem can be addressed by either exploiting transit prediction (in the case of cataloged objects), or by applying tailored criteria (for uncatalogued objects). The performance of the new architecture was assessed in real operational scenarios, demonstrating the enhancement represented by the implementation of the channelization strategy, as well as the angular measurement accuracy returned by MATER, in both nominal and off-nominal scenarios.

Modeling of Resident Space Object Light Curves with Blender Software

Modeling of Resident Space Object Light Curves with Blender Software

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

The recent proliferation of resident space objects (RSOs) in low Earth orbit (LEO) threatens the sustainability of space as a resource and requires persistent monitoring to avoid collisions involving valuable space assets. State-of-the-art ground-based space surveillance techniques, due to their susceptibility to atmosphere, weather, and lighting conditions, tend to focus on RSOs with characteristic length greater than 10 cm or 1 dm. Consequently, millions of smaller LEO RSOs remain untracked by ground-based methods, which reduces overall space situational awareness. Onboard satellite sensors offer a space-based method for tracking RSOs. Prior research has investigated the feasibility of using commercial star trackers (CSTs) — optical sensors prevalent on most active spacecraft — to observe, detect, and estimate the position and velocity of RSOs larger than 10 cm. In a recent effort, we expanded on these feasibility studies by assessing the capabilities of CSTs to detect debris particles smaller than 10 cm in characteristic length. We modeled the particles as Lambertian spheres with zero phase angle and ten percent reflectivity and found that typical CSTs can detect properly illuminated debris of characteristic length between 1 cm and 10 cm even at distances of tens of kilometers. More sensitive CSTs can characterize decimeter-scale RSOs hundreds of kilometers away; alternatively, they can track centimeter-scale and smaller RSOs at closer distances. In this paper, we summarize these key results and extend our previous study by relaxing its zero-phase-angle assumption and characterizing the effect of Sun-RSO-CST phase angle on debris detection range. We identify a number of representative CSTs with publicly available optical characteristics and consider the effects of properties such as pixel pitch, focal length, aperture diameter, and field of view (FOV) on the capability of each CST to detect debris at a given distance and relative velocity (in the form of streak speed). We find that, for debris particles modeled as diffuse Lambertian spheres, Sun-RSO-CST phase angles as high as 57° result in no more than 20% reduction to the useful RSO-CST detection range. In addition, we find that pixel pitch and focal length, rather than aperture diameter, tend to determine the capability of a given CST to resolve two distinct RSOs. Furthermore, streak speed may serve as a stronger limiting factor for detection of smaller debris particles than for larger ones. Despite these limitations, the overall results indicate that CSTs have the potential to substantially enhance space-based debris detection capability.

De-orbiting space debris via interaction between a permanent magnet and Earth’s magnetic field: A feasibility study

The accumulation of space debris is an ever-increasing problem. Systems that can reliably de-orbit (i.e., decrease the time to re-entry for) an inactive resident space object (RSO) are highly sought after. In this paper, we investigate the feasibility of utilizing the forces owing to the interaction of Earth’s magnetic field with a permanent magnet that has been attached to an RSO in low Earth orbit. We consider an actively controlled permanent magnet that is optimally oriented to remove energy from the system, and the most favorable magnet-to-RSO mass ratio. We show that, even under these best-case assumptions, the interaction between a permanent magnet and Earth’s magnetic field is not a viable means of de-orbiting an RSO.

Overview of High-Power and Wideband Radar Technology Development at MIT Lincoln Laboratory

This paper summarizes over 60 years of radar system development at MIT Lincoln Laboratory, from early research on satellite tracking and planetary radar to the present ability to perform the centimeter-resolution imaging of resident space objects and future plans to extend this capability to geosynchronous range.

Micro-Doppler Signature Analysis for Space Domain Awareness Using VHF Radar

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing.

Generation of initial debris cloud distributions for breakup events based on CARDC-SBM

With the dramatic increase in the number of resident space objects, the risk of collision between space objects and debris is growing. To protect spacecraft from such collisions, it is crucial to determine the generation patterns of space debris and analyze the evolution of debris clouds. In this paper, a continuous distribution formula is derived for each parameter of the breakup debris based on the China Aerodynamics Research and Development Center spacecraft breakup model (CARDC-SBM). Using CARDC-SBM, we theoretically establish a normal distribution for the separation velocity of the breakup debris in relation to the breakup mass center. Based on this foundation, the mean and standard deviation of the debris separation velocity distribution are empirically modeled using polynomial and exponential functions. Consequently, an empirical formula with a broad range of applicability is derived, which significantly reduces the computation time of debris cloud simulations. The initial velocity distribution given by this empirical formula is employed as the initial condition for the evolution of debris clouds from the perspective of a boundary value problem. Simulations using CARDC-SBM and subsequent analysis of the debris cloud evolution resulting from a breakup event reveal that the distribution of each parameter is determined solely by the relative collision velocity and the spacecraft's nominal density in the impact breakup scenario. Our results show that the empirical formula for the debris cloud distribution provides good agreement with Monte Carlo simulations.