Decentralized Global Satellite Tracking Network: Leveraging Crowd-sourced Astronomical Observations and Machine Learning for Cost Effective Orbital Monitoring

Deep Learning for Space Debris Tracking: One-Step Tracklet Filtering with a Hybrid GRU-CNN Architecture

The proliferation of space debris in Low Earth Orbit (LEO) poses a growing threat to operational satellites, requiring robust surveillance and tracking systems. Radar is a primary technology for monitoring these objects; however, standard tracking algorithms often degrade when measurements are sparse, corrupted by non-Gaussian noise, or available only as short tracklets. Traditional methods, such as the Unscented Kalman Filter (UKF), rely on explicit physical and statistical models that may struggle to converge under highly nonlinear dynamics, uncertain initialization, and non-ideal sensor perturbations. In this study, we propose a hybrid deep learning framework for learned one-step tracklet filtering of radar measurements. The architecture consists of a stateful Gated Recurrent Unit (GRU) layer followed by one-dimensional Convolutional Neural Network (1D-CNN) layers, complemented by variable-specific preprocessing strategies, including residual learning for range and relative pivoting for azimuth, to handle the scale disparities and heterogeneous behavior of radar observables. This design combines the ability of GRUs to model temporal dependencies with the effectiveness of CNNs in extracting local features for signal denoising. The method is validated on synthetically generated LEO trajectories with realistic orbital perturbations and tunable radar noise profiles, including Gaussian noise, impulsive spikes, transient degradation, and state-dependent perturbations. Compared with EKF- and UKF-based analytical baselines, the proposed model achieves lower filtering error and improved robustness under severe non-Gaussian disturbances. Stress testing further shows that the network can reject non-physical sensor anomalies without requiring long initialization warm-up phases, making it suitable for sparse short-tracklet processing in synthetic SST benchmark scenarios.

LEO Satellite Network Systems for Instantaneously Detecting Environmental Changes

Generating Two-Line Elements of Celestial Objects From Astronomical Images Using Deep Neural Networks

Accurate satellite tracking requires up-to-date Two-Line Elements (TLEs), as outdated data can lead to significant positioning errors. While ground-based optical telescopes are highly accessible, generating TLEs from their data is complicated by the fundamental lack of direct range measurements. This paper presents an automated end-to-end pipeline designed to overcome this limitation by proposing a robust method to estimate the range from prior TLE. The pipeline can then generate the updated TLEs by calculating new satellite state vectors using the estimated range. The pipeline consists of star and satellite detection, astrometric calibration, and orbit determination. For star and satellite detection, the key component of the pipeline is a robust deep learning-based detection model. To achieve this, we benchmarked models such as Deformable DETR, RF-DETR, and YOLOv12 against traditional image processing methods with 3105 images in FITS (Flexible Image Transport System) format. RF-DETR yields the highest F1 score (0.93) and precision (0.97) at an 8-pixel threshold. The detected star coordinates resulting from using RF-DETR, the best-performing model, were fed into Astrometry.net for precise astrometric calibration to determine the satellite celestial coordinates. The range required for orbit determination was estimated by extracting the prior TLE and propagating it to the observation epoch via SGP4. The satellite state vectors were then calculated using TLE-constrained orbit determination approach using the estimated range, followed by an inverse SGP4 optimization to recover the mean orbital elements. The generated TLEs were validated against public TLEs from Space-Track.org. To evaluate this pipeline, updated TLEs were generated specifically for medium Earth orbit (MEO) and geostationary Earth orbit (GEO) targets. The results demonstrate that the proposed method yields high accuracy for GEO satellites, achieving a mean motion difference of 0.0030 rev/day and a 24-hour ground-track position error of 0.83 degrees. In comparison, MEO satellites achieve a mean motion difference of 0.0126 rev/day and an error of 5.27 degrees. These results suggest that the proposed pipeline provides a robust foundation for automated orbit determination with clear potential for further refinement.

Using Deep Learning–Extracted Road Networks for More Accurate Small Satellite Geometric Correction

The upgraded version of the geometric correction module of the STORM processing chain can automatically orthorectify images from the NEMO-HD small satellite, which, like other small satellites, in principle has a lower signal-to-noise ratio (SNR) and higher radiometric variability.

A Machine Learning Approach to Monitor Satellite Constellation Orbits

Keeping a satellite in the right orbit is no easy feat. It’s constantly influenced by various forces like Earth’s uneven gravity, air resistance high up in the atmosphere, and even pressure from sun-light. Over time, these factors can nudge satellites off their intended paths. Nowadays, most satel-lites depend on ground control teams to keep an eye on their orbits and plan any necessary adjust-ments. This can be a slow, expensive, and tricky process, especially as the number of satellites in space keeps increasing. This paper outlines a machine learning approach to check for deviation from the intended orbit of a given satellite con-stellation. The system continuously tracks essen-tial orbital data, such as position and velocity, to see if a satellite is veering off course. To develop and test this method, we used publicly available data from Starlink satellites and other spacecraft. Starlink satellites are particularly valuable because they frequently adjust their orbits and operate in large clusters, providing rich and dynamic data. We trained a machine learning model to predict if a satellite has left from its constellation. By com-paring this prediction with the actual position of the satellite, the system can spot when the orbit starts to drift. The findings indicate that blend-ing basic physics with machine learning can ef-fectively aid in autonomous orbit correction. This approach holds promise for cutting costs, boost-ing efficiency, and simplifying the management of a growing number of satellites in the future.

Improved detection accuracy and efficiency for small-object detection in drone imagery with lightweight LightS-DETR

Compared to existing state-of-the-art methods, LightS-DETR delivers superior detection accuracy and robust real-time performance while significantly reducing computational demands, offering an optimal balance for UAV deployment.