Maneuver detection and estimation is deemed crucial for maintaining catalogs of Resident Space Objects (RSOs) as it helps to avoid sets of duplicated objects and track correlation issues. In fact, maneuvers, along with launches and break-up events, are the main source of potential new object detections during RSOs cataloging activities. For the continuous and reliable provision of Space Situational Awareness (SSA) and Space Traffic Management (STM) services, a challenging trade-off between detection time and characterization accuracy of maneuvers needs to be performed. In this paper, two novel and operationally feasible methodologies are proposed for maneuver detection and estimation. The first, a track-to-orbit methodology, uses a pre-maneuver orbit to linearize the dynamics and estimate the single burn that minimizes the residuals of the post-maneuver tracks. The second, an orbit-to-orbit methodology, estimates the double burn that solves a minimization problem between the pre-maneuver and post-maneuver orbits. Both methods, based on an optimal control approach, are not only proposed to tackle the maneuver estimation problem but also to be integrated on operational and robust association frameworks. Results are presented for optical scenarios with both simulated and real data, providing insightful conclusions on the capabilities, performance and limitations of the proposed methods. Particular emphasis is given to the importance of the track association, since a single track is usually not enough to perform a reliable estimation of the maneuver. Besides, the capability of the methods to provide a solution to the association problem, even when not perfectly characterizing the true maneuver, is discussed.

A systematic analysis of the role played by several physical mechanisms in the longitudinal stability of a tethered kite is presented. A simple model, which artificially constrains the pitch motion...

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 $$\chi ^2$$χ 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.

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.