Earth Rotation-Aware Non-Stationary Satellite Communication Systems: Modeling and Analysis

Ye, Jia; Pan, Gaofeng; Alouini, Mohamed‐Slim

doi:10.1109/twc.2021.3071377

Cited by 16 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These links must be realised with a moving infrastructure. NGSO satellites move rapidly with respect to each other in higher and lower orbits and in different orbital planes.They also move with respect to the Earth due to the satellite orbital velocity and to the Earth's rotation [20]. Specifically, the orbital velocity of the satellites v o is determined by the altitude of deployment…”

Section: Communication Linksmentioning

confidence: 99%

See 1 more Smart Citation

NGSO Constellation Design for Global Connectivity

Leyva-Mayorga¹,

Soret²,

Matthiesen³

et al. 2022

Preprint

View full text Add to dashboard Cite

Section: Communication Linksmentioning

confidence: 99%

“…Finally, the whole satellite constellation is moving w.r.t. the Earth due to its rotation [20], [2]. Hence, an important aspect to select the altitude of deployment of a constellation is whether it is desired that the orbit is recursive.…”

Section: Communication Linksmentioning

confidence: 99%

NGSO Constellation Design for Global Connectivity

Leyva-Mayorga¹,

Soret²,

Matthiesen³

et al. 2022

Preprint

View full text Add to dashboard Cite

“…As shown in Figure 1, a single satellite can provide two measurements of the target through processing SAR images, consisting of the azimuth, 𝜙 , and the elevation angle, 𝜃. The detecting coordinate system is paralleled to the Earth-Centered Inertial (ECI) [34]. For satellite 𝑠 , the measurement model at time 𝑘 is described as Equation ( 6):…”

Section: Measurement Modelmentioning

confidence: 99%

Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target

Zhou¹,

Wang²,

Zheng³

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.

show abstract

“…Contrary to satellites in geostationary (GEO), these require a high orbital velocity to maintain their orbit and, hence, move rapidly with respect to the Earth's surface. Furthermore, the orbits of LEO satellites do not compensate for the rotation of the Earth [7]. Hence, every satellite pass might occur at a different position with respect to the ground infrastructure, which creates the need for continuous adaptation and management of the radio resources.…”

Section: Fundamentals On Satellite Constellationsmentioning

confidence: 99%

Federated Learning in Satellite Constellations

Matthiesen¹,

Razmi²,

Leyva-Mayorga³

et al. 2022

Preprint

View full text Add to dashboard Cite

Distributed machine learning (DML) results from the synergy between machine learning and connectivity. Federated learning (FL) is a prominent instance of DML in which intermittently connected mobile clients contribute to the training of a common learning model. This paper presents the new context brought to FL by satellite constellations where the connectivity patterns are significantly different from the ones assumed in terrestrial FL. We provide a taxonomy of different types of satellite connectivity relevant for FL and show how the distributed training process can overcome the slow convergence due to long offline times of clients by taking advantage of the predictable intermittency of the satellite communication links. Recent developments strive to alleviate these downsides and bring the training process towards B. Matthiesen, N. Razmi, and A. Dekorsy are with the Gauss-Olbers Center, c

show abstract

Earth Rotation-Aware Non-Stationary Satellite Communication Systems: Modeling and Analysis

Cited by 16 publications

References 35 publications

NGSO Constellation Design for Global Connectivity

NGSO Constellation Design for Global Connectivity

Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target

Federated Learning in Satellite Constellations

Contact Info

Product

Resources

About