Adaptive track estimation on a radar array system for space surveillance

Montaruli, Marco Felice; Facchini, Luca; Lizia, P. Di; Massari, Mauro; Pupillo, G.; Bianchi, G.; Naldi, G.

doi:10.1016/j.actaastro.2022.05.051

Cited by 19 publications

(13 citation statements)

References 9 publications

(17 reference statements)

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“…However, the median and interquartile range for the estimation errors of XGBoost models is the least among all the models. The median and interquartile range of the estimation errors of the Lagrange method are the highest among all the methods.We have found through experiments that the output is sensitive to position noise standard deviation compared to the standard deviation for velocity and mass noises 1 .…”

Section: Resultsmentioning

confidence: 99%

“…Since the launch of Sputnik in 1957, approximately 13,000 satellites have been deployed in the Low Earth orbit 1 . With collisions, explosions and fragmentations, the number of space debris sized less than 1 cm is now estimated to be more than 1,000,000 1 . The US Space Surveillance Network has a catalogue of about 30,000 resident space objects only 1 .…”

mentioning

confidence: 99%

“…With collisions, explosions and fragmentations, the number of space debris sized less than 1 cm is now estimated to be more than 1,000,000 1 . The US Space Surveillance Network has a catalogue of about 30,000 resident space objects only 1 . Since 97% of space debris is not tracked, they pose a greater threat to active satellites as well as future space missions.…”

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confidence: 99%

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Tracking an untracked space debris after an inelastic collision using physics informed neural network

Singh,

Kumar

et al. 2024

Sci Rep

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With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris’ trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN)—based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. To obtain the states of the active satellite, we use the TLE data of 1647 Starlink and 66 LEMUR satellites obtained from space-track.org. The velocity of space debris is initialized using our proposed velocity sampling method, and the coefficient of restitution is sampled from our proposed Gaussian mixture-based probability density function. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN) based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better estimation performance for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Tracking an untracked space debris after an inelastic collision using physics informed neural network

Singh,

Kumar

et al. 2024

Sci Rep

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“…In this way, some orbits may become impractical for several years [5]. Objects bigger than some centimeters can be tracked from ground [6,7] and therefore it is possible to predict an impact and perform avoidance maneuvers against catastrophic fragmentations [8,9]. Nevertheless, even smaller fragments, down to a few millimeters or less in size, can still pose a risk to operational satellites and spacecraft.…”

Section: Mission Overviewmentioning

confidence: 99%

Concept and Feasibility Analysis of the Alba Cubesat Mission

Mozzato,

Bemporad,

Enzo

et al. 2024

Aerotec. Missili Spaz.

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AlbaSat is a 2-Unit CubeSat which is being developed by a student team at the University of Padova. The Alba project aims to design, build, test, launch, and operate the first student CubeSat of the University of Padova, featuring four different payloads. The first goal is to collect data regarding the debris environment in Low Earth Orbit, the second goal is the study of the satellite vibrations, the third one is about CubeSat attitude determination through laser ranging technology, and the fourth goal concerns satellite laser and quantum communication. The Alba CubeSat mission has been selected by the European Space Agency to join the Fly Your Satellite! Design Booster program in December 2022. This paper presents the feasibility study of the Alba CubeSat mission reproduced in the framework of the “Space Systems Laboratory” class of Master of Science in Aerospace Engineering at the University of Padova. In the beginning, a mission requirements definition was conducted. After that, the mission feasibility was considered, with preliminary requirements verification to assess the ability of the spacecraft to survive the space environment, including compliance with Debris Mitigation Guidelines, ground station visibility and minimum operative lifetime evaluation. The Alba mission sets a base for a better understanding of the space environment and its interaction with nanosatellites, and an improvement of the accuracy of debris models. Furthermore, this paper, describing the educational experience and the results achieved, will provide a useful example for future students’ studies on CubeSat mission design.

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“…The MUltiple SIgnal Classification (MUSIC) algorithm is a high-resolution method based on spatial spectrum estimation, designed to perform unbiased estimation using the orthogonality of signal and noise subspaces [ 20 , 21 ]. The MUSIC algorithm, by employing a dense sensor array configuration, not only facilitates deployment within complex composite material structures and confined spaces, but also enables extensive structural direction scanning, covering a broad monitoring range.…”

Section: Introductionmentioning

confidence: 99%

Guided Lamb Wave Array Time-Delay-Based MUSIC Algorithm for Impact Imaging

Zheng,

Yuan

2024

Sensors

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Composite materials, valued in aerospace for their stiffness, strength and lightness, require impact monitoring for structural health, especially against low-velocity impacts. The MUSIC algorithm, known for efficient directional scanning and easy sensor deployment, is gaining prominence in this area. However, in practical engineering applications, the broadband characteristics of impact response signals and the time delay errors in array elements’ signal reception lead to inconsistencies between the steering vector and the actual signal subspace, affecting the precision of the MUSIC impact localization method. Furthermore, the anisotropy of composite materials results in time delay differences between array elements in different directions. If the MUSIC algorithm uses a fixed velocity value, this also introduces time delay errors, further reducing the accuracy of localization. Addressing these challenges, this paper proposes an innovative MUSIC algorithm for impact imaging using a guided Lamb wave array, with an emphasis on time delay management. This approach focuses on the extraction of high-energy, single-frequency components from impact response signals, ensuring accurate time delay measurement across array elements and enhancing noise resistance. It also calculates the average velocity of single-frequency components in varying directions for an initial impact angle estimation. This estimated angle then guides the selection of a specific single-frequency velocity, culminating in precise impact position localization. The experimental evaluation, employing equidistantly spaced array elements to capture impact response signals, assessed the effectiveness of the proposed method in accurately determining array time delays. Furthermore, impact localization tests on reinforced composite structures were conducted, with the results indicating high precision in pinpointing impact locations.

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Adaptive track estimation on a radar array system for space surveillance

Cited by 19 publications

References 9 publications

Tracking an untracked space debris after an inelastic collision using physics informed neural network

Tracking an untracked space debris after an inelastic collision using physics informed neural network

Concept and Feasibility Analysis of the Alba Cubesat Mission

Guided Lamb Wave Array Time-Delay-Based MUSIC Algorithm for Impact Imaging

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