https://backend.orbit.dtu.dk/ws/files/247661618/1_s2.0_S0273117721000594_main.pdf

Altimetry for the future: building on 25 years of progress

The Sentinel-3 mission takes routine measurements of sea surface heights and depends crucially on accurate and precise knowledge of the spacecraft. Orbit determination with a targeted uncertainty of less than 2 cm in radial direction is supported through an onboard Global Positioning System (GPS) receiver, a Doppler Orbitography and Radiopositioning Integrated by Satellite instrument, and a complementary laser retroreflector for satellite laser ranging. Within this study, the potential of ambiguity fixing for GPS-only precise orbit determination (POD) of the Sentinel-3 spacecraft is assessed. A refined strategy for carrier phase generation out of low-level measurements is employed to cope with half-cycle ambiguities in the tracking of the Sentinel-3 GPS receiver that have so far inhibited ambiguity-fixed POD solutions. Rather than explicitly fixing double-difference phase ambiguities with respect to a network of terrestrial reference stations, a single-receiver ambiguity resolution concept is employed that builds on dedicated GPS orbit, clock, and wide-lane bias products provided by the CNES/CLS (Centre National d’Etudes Spatiales/Collecte Localisation Satellites) analysis center of the International GNSS Service. Compared to float ambiguity solutions, a notably improved precision can be inferred from laser ranging residuals. These decrease from roughly 9 mm down to 5 mm standard deviation for high-grade stations on average over low and high elevations. Furthermore, the ambiguity-fixed orbits offer a substantially improved cross-track accuracy and help to identify lateral offsets in the GPS antenna or center-of-mass (CoM) location. With respect to altimetry, the improved orbit precision also benefits the global consistency of sea surface measurements. However, modeling of the absolute height continues to rely on proper dynamical models for the spacecraft motion as well as ground calibrations for the relative position of the altimeter reference point and the CoM.

/pdf/precise-orbit-determination-of-the-sentinel-3a-altimetry-34362fflje.pdf

Precise orbit determination of the Sentinel-3A altimetry satellite using ambiguity-fixed GPS carrier phase observations

Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical climates. This issue has become more severe and increasingly occurred in the Mekong River Delta of Vietnam. The main objective of this work is to map soil salinity intrusion in Ben Tre province located on the Mekong River Delta of Vietnam using the Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined with five state-of-the-art machine learning models, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Gaussian Processes (GP), Support Vector Regression (SVR), and Random Forests (RF). For this purpose, 63 soil samples were collected during the field survey conducted from 4–6 April 2018 corresponding to the Sentinel-1 SAR imagery. The performance of the five models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r). The results revealed that the GP model yielded the highest prediction performance (RMSE = 2.885, MAE = 1.897, and r = 0.808) and outperformed the other machine learning models. We conclude that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change.

Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

Satellite laser ranging (SLR) to low Earth orbiters (LEOs) provides optical distance measurements with mm-to-cm-level precision. SLR residuals, i.e., differences between measured and modeled ranges, serve as a common figure of merit for the quality assessment of orbits derived by radiometric tracking techniques. We discuss relevant processing standards for the modeling of SLR observations and highlight the importance of line-of-sight-dependent range corrections for the various types of laser retroreflector arrays. A 1–3 cm consistency of SLR observations and GPS-based precise orbits is demonstrated for a wide range of past and present LEO missions supported by the International Laser Ranging Service (ILRS). A parameter estimation approach is presented to investigate systematic orbit errors and it is shown that SLR validation of LEO satellites is not only able to detect radial but also along-track and cross-track offsets. SLR residual statistics clearly depend on the employed precise orbit determination technique (kinematic vs. reduced-dynamic, float vs. fixed ambiguities) but also reveal pronounced differences in the ILRS station performance. Using the residual-based parameter estimation approach, corrections to ILRS station coordinates, range biases, and timing offsets are derived. As a result, root-mean-square residuals of 5–10 mm have been achieved over a 1-year data arc in 2016 using observations from a subset of high-performance stations and ambiguity-fixed orbits of four LEO missions. As a final contribution, we demonstrate that SLR can not only validate single-satellite orbit solutions but also precise baseline solutions of formation flying missions such as GRACE, TanDEM-X, and Swarm.

/pdf/satellite-laser-ranging-to-low-earth-orbiters-orbit-and-1evzssrwwi.pdf

Satellite laser ranging to low Earth orbiters: orbit and network validation

https://repository.tudelft.nl/islandora/object/uuid%3A08b8f546-7b34-4231-9bfd-4aa61835d88e/datastream/OBJ/download

Sentinel-1A - First precise orbit determination results

Initial orbit determination methods for track-to-track association

Improving orbital uncertainty realism through covariance determination

Abstract 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. 

https://link.springer.com/content/pdf/10.1007/s40295-022-00311-5.pdf

Satellite maneuver detection and estimation with optical survey observations

Abstract 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. 

/pdf/track-to-track-association-methodology-for-operational-1xzxxz91.pdf

Diego Escobar

Papers

Sentinel-1A - First precise orbit determination results

Initial orbit determination methods for track-to-track association

Improving orbital uncertainty realism through covariance determination

Satellite maneuver detection and estimation with optical survey observations

Track-to-track association methodology for operational surveillance scenarios with radar observations