Bayesian detection and classification for space-augmented space situational awareness under intermittent communications

Active debris multi-removal mission concept based on hybrid propulsion

Knowledge about the attitude state of space debris is necessary for the design of active debris removal missions. Ground-based, passive optical observations may be used to improve the accuracy of orbital predictions and allow studying the attitude motion of spinning passive objects in orbit. The application of developed methods can be extended to contingency scenarios (failure investigation) or even to active mission, for example for the validation of sensors. Unlike other methods that use retro reflectors on the target or complex radar facilities, the method used in this work requires only a telescope equipped for relative 1 photometry. Acquisition routine should include tracking algorithms for the targeted orbits. Other teams using similar methods studied primarily the large objects, which spin fast or which move slowly in the sky. Challenges related to acquisition and processing increase when small objects in low Earth orbit are studied as the phase angle varies quickly with time, the signal can be very faint and the spin axis is typically less stable due to external perturbations. In addition, the complexity of the analysis increases when the targets are spinning slowly. Between April and July 2016, SwissCube, a 10cm CubeSat type spacecraft designed, built and operated by EPFL (Ecole polytechnique federale de Lausanne) and other Swiss partner institutions, has been intensively observed by the Astronomical Institute of the University of Bern (AIUB). EPFL was retrieving internal attitude data and performing radio signal amplitude analysis for the time intervals of the AIUB optical observations. These three independent sources of attitude estimation were combined for validation purposes. The spin rate has been actively changed during this period in order to test the limits of this method for different spin periods. This paper presents the observation (photometry and radio signal) and the data processing techniques in the first section. It then describes the SwissCube sensors and their configuration in the Cube. Finally, we present the ob- servations and cross-correlation results. The results are encouraging, especially for SwissCube spin rates greater than 6 deg/sec.

Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; in particular, they often feature large apertures with low‐noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high‐power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi‐element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high‐fidelity rotation period estimation and determination of minimum target size.

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.

The rapid proliferation of resident space objects in low Earth orbit has rendered traditional collision avoidance workflows increasingly inadequate for the scale and operational tempo of modern constellation management. This paper presents OrbiFM, a foundation model (FM)-based framework for autonomous collision avoidance in congested orbital environments. OrbiFM integrates a multi-modal transformer encoder with a physically constrained risk assessment head and an autoregressive maneuver decoder, processing conjunction data messages (CDM), two-line element (TLE)-derived orbital states, and space weather indices within a unified architecture adapted through low-rank adaptation (LoRA) fine-tuning. Simulation experiments across a synthetic catalog of 2,400 low Earth orbit (LEO) objects demonstrate that OrbiFM achieves a mean collision probability prediction error of 3.2%, a false positive maneuver trigger reduction of 12.2% relative to recurrent neural network baselines, and a per-satellite fuel saving of 18.6% over a 90-day evaluation window. Chain-of-thought inference additionally enables humaninterpretable decision justification, a critical prerequisite for regulatory trust in autonomous space traffic management systems.

This paper shows a novel method to characterize human-made objects in low Earth orbit (LEO) using compressed sensing on light curve measurements. The proposed approach minimizes total variation to recover a resolved object image from a fully unresolved light curve and a so-called point spread function (PSF) map. The light curves are generated through numerical wave propagation, which considers atmospheric turbulence under anisoplanatic conditions. Subsequently, the light curve model is transformed into a linear measurement model to apply compressed sensing techniques. Notably, the sensing matrix is found to be a superposition of spatially variable PSFs, which significantly downsamples the ideal object image. The proposed approach robustly recovers clear images of objects in LEO, even with imperfect PSF map estimates and Poisson noise in the light curve measurement.

The non-functioning spacecraft COSMOS 1408 (NSSDCA/COSPAR ID: 1982-092A) was destroyed as a result of tests involving Russian anti-satellite weapons on 15 November 2021. This led to the creation of a significant debris cloud that poses a threat to other objects in low Earth orbit (LEO), particularly those in close orbits. More than 1500 of these fragments reached trackable sizes. Such events require rapid and immediate monitoring through all available ground tracking means, including radar and optical observation. The following study presents the results of optical observations of the selected fragments of COSMOS 1408 in Ukraine. These observations were carried out by the telescope OES30 from the National Space Facilities Control and Test Center of the State Space Agency of Ukraine and the Fast Robotic Telescope (FRT) of the Research Institute "Mykolaiv Astronomical Observatory" in February 2022. The observations demonstrated that Ukrainian optical sensors are capable of tracking LEO space debris objects with radar cross-sections (RCS) less than 0.1 square meters when relatively accurate ephemeris data is available. The astrometric reduction has been performed for the acquired frames with fragments of the satellite, which revealed that orbital parameters of a significant part of the targeting objects were close to the orbital parameters of the original satellite before the event. Furthermore, the results of processing the available observations indicated that the range of apogee heights significantly exceeds the range of perigee heights. These findings align with conclusions drawn by previous researchers. In the future, it will be essential to assess the capabilities of sensors for observing the aftermath of object destruction in LEO, especially during the initial hours and days following such events when precise debris orbit data may be lacking.

Orbit determination (OD) is a fundamental problem in space surveillance and tracking, crucial for ensuring the safety of space assets. Real-world ground-based optical tracking scenarios often involve challenges such as limited measurement time, short visible arcs, and the presence of outliers, leading to sparse and non-Gaussian observational data. Additionally, the highly perturbative and nonlinear orbit dynamics of resident space objects (RSOs) in low Earth orbit (LEO) add further complexity to the OD problem. This article introduces a variant of the higher order unscented Kalman estimator (HOUSE) called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$w$</tex-math></inline-formula>-HOUSE, which employs a square-root formulation and addresses the challenges posed by nonlinear and non-Gaussian OD problems. The effectiveness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$w$</tex-math></inline-formula>-HOUSE was demonstrated through synthetic and real-world measurements, specifically outlier-contaminated angle-only measurements collected for the Sentinel 6 A satellite flying in LEO. Comparative analyzes are conducted with the original HOUSE (referred to as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-HOUSE), unscented Kalman filters (UKF), conjugate unscented transformation (CUT) filters, and precise OD solutions estimated via onboard global navigation satellite system measurements. The results reveal that the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$w$</tex-math></inline-formula>-HOUSE filter exhibits greater robustness when dealing with varying values of the dependent parameter compared to the original <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-HOUSE. Moreover, it surpasses all other filters in terms of positioning accuracy, achieving 3-D root-mean-square errors of less than 60 m in a three-day scenario. This research suggests that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$w$</tex-math></inline-formula>-HOUSE filter represents a viable alternative to UKF and CUT filters, offering improved positioning performance in handling the nonlinear and non-Gaussian OD problems associated with LEO RSOs.

Spacecraft attitude control subsystem: Reliability, multi-state analyses, and comparative failure behavior in LEO and GEO

Optimal sensor management of dispersed and disparate sensors for tracking Low Earth Orbit (LEO) objects presents a daunting theoretical and practical challenge since it requires the optimal utilization of different types of sensors and platforms that include Ground Based Radars (GBRs) positioned throughout the globe, and the Space Based Visible (SBV) sensor on board LEO platforms. We derive and demonstrate new computationally efficient algorithms for multisensor-multiobject tracking of LEO objects. The algorithms are based on the Posterior Expected Number of Objects as the sensor management objective function, observation models for the sensors/platforms, and the Probability Hypothesis Density Particle Filter (PHD-PF) tracker.

This paper presents the design, simulation, and experimental validation of a dual-Circularly Polarized (CP) array antenna to be used as single element for a bistatic radar system, aimed at detecting and tracking objects in Low Earth Orbit (LEO). The antenna operates at 412 MHz in reception mode and consists of an array of 19 slotted-patch radiating elements with a cavity-based metallic superstrate, designed to support dual circular polarization. These elements are arranged in a hexagonal configuration, enabling the array structure to achieve a maximum realized gain of 17 dBi and a Side Lobe Level (SLL) below −17 dB while maintaining high polarization purity. Two identical analog feeding networks enable the precise control of phase and amplitude, allowing the independent reception of Right-Hand and Left-Hand Circularly Polarized (RHCP and LHCP) signals. Full-wave simulations and experimental measurements confirm the high performance and robustness of the system, demonstrating its suitability for integration into large-scale Space Situational Awareness (SSA) sensor networks.

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.

Detecting and tracking objects in low Earth orbit is an increasingly important task. Telescope observations contribute to its accomplishment, and telescope imagers produce a large amount of data for this task. Thus, it is convenient to use fast computer-aided processes to analyze it. Telescopes tracking at the sidereal rate usually detect these objects in their imagers as streaks, their lengths depending on the exposure time and the slant range to the object. We have developed a processing pipeline to automatically detect streaks in astronomical images in real time (i.e., faster than the images are produced) by a graphics processing unit parallel processing system. After the detection stage, streak photometric information is obtained, and object candidate identification is provided through matches with a two-line element set database. The system has been tested on a large set of images, consisting of two hours of observation time, from the Tomo-e Gozen camera of the 105 cm Schmidt telescope at Kiso Observatory in Japan. Streaks were automatically detected in approximately 0.5% of the images. The process detected streaks down to a minimum apparent magnitude of +11.3 and matched the streaks with objects from the space-track catalog in 78% of the cases. We believe that this processing pipeline can be instrumental in detecting new objects and tracking existing ones when processing speed is important, for instance, when a short handover time is required between follow-up observation stations, or when there is a large number of images to process. This study will contribute to consolidating optical observations as an effective way to control and alleviate the space debris problem.

Optical orbital debris spotter

We have searched for the hypothetical small comets proposed by Frank et al. [1986a, b] and Frank and Sigwarth [1993] using the world's most powerful radar in terms of gain‐aperture product. The Naval Space Surveillance System can detect most space objects in low Earth orbit with radar cross sections (RCSs) of 0.1 m2 or larger; at higher altitudes of the order of 10,000–20,000 km the radar can detect objects with RCSs of 1 m2. We carried out detailed first‐principle calculations of the RCS of spherical comet using the properties proposed by Frank and Sigwarth [1993]. We find that 8–12 m diameter comets have an average cross section of 0.4 m2 at the radar frequency (217 MHz), with peaks reaching 1 m2. Therefore the Naval radar system has sufficient sensitivity to detect many small comets, especially as they approach low Earth orbit. We estimate that at least 800–5000 small comets should have been detected by the radar during the 37 day search period during fall 1997. None of the more than 12,000 unidentified detections can be explained by small comets. The lack of detection of small comets by the radar can be explained only if small comets have RCSs &lt;0.1% of their assumed physical size (which is unrealistic, given that human technology can match this value only by tailoring a design for a specific radar) or if their impact rate with Earth is some 4 orders of magnitude less than proposed by Frank et al. [1986a] and Frank and Sigwarth [1993].