A Novel Simulation Modeling Method and Hardware Implementation for Doppler Power Spectrum of LEO Satellite Based on Error Compensations by Parting Sinusoid with Random AOA and Correlation Piecewise Convergence

Low Earth Orbit (LEO) Satellite Internet Network (LEO-SIN) is a promising approach to global Gigabit per second (Gbps) broadband communications in the coming sixth-generation (6G) era. This paper mainly focuses on the innovation of accuracy improvement of simulation modeling of the Doppler Power Spectrum (DPS) of satellite channels in LEO-SIN. Existing DPS modeling methods are based on Rice’s Sum-of-Sinusoids (SOS) which have obvious modeling errors in scenarios with main signal propagation paths, asymmetrical power spectrum, and random multi-path signals with a random Angle of Arrival (AOA) in LEO-SIN. There are few state-of-art researches devoted to higher accuracy of DPS modeling for simulation. Therefore, this paper proposes a novel Random Method of Exact Doppler Spread method Set Partitioning (RMEDS-SP). Distinct from existed researches, we firstly model the DPS of LEO-SIN, which more accurately describes the characteristics of frequency dispersion with the main path and multi-path signals with random AOA. Furthermore, piecewise functions to the Autocorrelation Function (ACF) of RMEDS-SP is first exploited to converge the modeling error supposition with time by periodic changes, which further improve the accuracy of the DPS model. Experimental results show that the accuracy of DPS in our proposed model is improved by 32.19% and 18.52%, respectively when compared with existing models.

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Multi-Scene Doppler Power Spectrum Modeling of LEO Satellite Channel Based on Atlas Fingerprint Method

IEEE Access ◽

10.1109/access.2021.3051774 ◽

2021 ◽

Vol 9 ◽

pp. 11811-11822

Author(s):

Wen Liu ◽

Shixuan Zheng ◽

Zhongliang Deng ◽

Ke Wang ◽

Wenliang Lin ◽

...

Keyword(s):

Power Spectrum ◽

Spectrum Modeling ◽

Doppler Power ◽

Leo Satellite

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An Estimator for Weather Radar Doppler Power Spectrum via Minimum Mean Square Error

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3044111 ◽

2021 ◽

pp. 1-16

Author(s):

Eiichi Yoshikawa ◽

Naoya Takizawa ◽

Hiroshi Kikuchi ◽

Tomoaki Mega ◽

Tomoo Ushio

Keyword(s):

Power Spectrum ◽

Mean Square Error ◽

Minimum Mean Square Error ◽

Weather Radar ◽

Mean Square ◽

Doppler Power

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The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

Remote Sensing ◽

10.3390/rs13091702 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1702

Author(s):

Kévin Barbieux ◽

Olivier Hautecoeur ◽

Maurizio De Bartolomei ◽

Manuel Carranza ◽

Régis Borde

Keyword(s):

Land Surface ◽

Weather Prediction ◽

Forecast Model ◽

Low Earth Orbit ◽

Reference Image ◽

Motion Vectors ◽

Infrared Imager ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

Leo Satellite

Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022.

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Simulation study of the plasma-brake effect

Annales Geophysicae ◽

10.5194/angeo-32-1207-2014 ◽

2014 ◽

Vol 32 (10) ◽

pp. 1207-1216 ◽

Cited By ~ 12

Author(s):

P. Janhunen

Keyword(s):

Magnetic Field ◽

High Performance ◽

Ionospheric Plasma ◽

Low Earth Orbit ◽

Earth Orbit ◽

Breaking Force ◽

Field Orientation ◽

Particle In Cell ◽

Leo Satellite ◽

The Magnetic Field

Abstract. Plasma brake is a thin, negatively biased tether that has been proposed as an efficient concept for deorbiting satellites and debris objects from low Earth orbit. We simulate the interaction with the ionospheric plasma ram flow with the plasma-brake tether by a high-performance electrostatic particle in cell code to evaluate the thrust. The tether is assumed to be perpendicular to the flow. We perform runs for different tether voltage, magnetic-field orientation and plasma-ion mass. We show that a simple analytical thrust formula reproduces most of the simulation results well. The interaction with the tether and the plasma flow is laminar (i.e. smooth and not turbulent) when the magnetic field is perpendicular to the tether and the flow. If the magnetic field is parallel to the tether, the behaviour is unstable and thrust is reduced by a modest factor. The case in which the magnetic field is aligned with the flow can also be unstable, but does not result in notable thrust reduction. We also correct an error in an earlier reference. According to the simulations, the predicted thrust of the plasma brake is large enough to make the method promising for low-Earth-orbit (LEO) satellite deorbiting. As a numerical example, we estimate that a 5 km long plasma-brake tether weighing 0.055 kg could produce 0.43 mN breaking force, which is enough to reduce the orbital altitude of a 260 kg object mass by 100 km over 1 year.

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Satellite-Aerial Integrated Computing in Disasters: User Association and Offloading Decision

10.36227/techrxiv.11396760 ◽

2020 ◽

Author(s):

Long Zhang ◽

Hongliang Zhang ◽

Chao Guo ◽

Haitao Xu ◽

Lingyang Song ◽

...

Keyword(s):

Low Earth Orbit ◽

User Association ◽

Integer Linear Programming Problem ◽

Hypergraph Matching ◽

Hypergraph Model ◽

High Altitude Platforms ◽

Np Complete ◽

Sum Rate ◽

Vertex Disjoint ◽

Leo Satellite

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

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Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

Wireless Communications and Mobile Computing ◽

10.1155/2018/3026405 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Zhenyu Na ◽

Zheng Pan ◽

Xin Liu ◽

Zhian Deng ◽

Zihe Gao ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Low Earth Orbit ◽

Traffic Prediction ◽

Satellite Network ◽

Integrated Networks ◽

Distributed Routing ◽

Learning Machine ◽

Terrestrial Communications ◽

Leo Satellite

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

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Data pre-processing for ionosphere TEC retrieval based on DORIS observations

10.5194/egusphere-egu2020-21943 ◽

2020 ◽

Author(s):

Shaocheng Zhang ◽

Wei Li ◽

Fei Yin ◽

Hongfei Gou

Keyword(s):

Precise Orbit Determination ◽

Low Earth Orbit ◽

Cycle Slip ◽

Dual Frequency ◽

Phase Measurements ◽

Cycle Slip Detection ◽

Low Earth Orbit Satellites ◽

Signal Path ◽

Leo Satellite

&#160;DORIS system aims to provide precise orbit determination of low earth orbit satellites, and the dual-frequencies on S1=2036.25 MHz and U2=401.25 MHz were used on DORIS signals. The ionosphere TEC retrieval on the signal path is possible based on DORIS dual-frequency observations.Analysis results show that DORIS pseudo-ranges had noise with several kilometers level, hence only the carrier-phase observations could be utilized on TEC retrieval. Moreover, as the DORIS ground stations were thousands kilometers separated with each other, station differential cannot be guaranteed and the data preprocessing can only be done base on the un-difference observations before the TEC could be precisely determined.In this research, a polynomial function was applied to model the DORIS phase observations, and minimal detectable biases (MDB) of less than one cycle wavelength was used as the index on the cycle-slip detection. And then the geometry free combination of S1 and U2 phase measurements were calculated for each DORIS LEO satellite passing arc. Finally, the unknown ambiguities bias on S1 and U2 geometry free observables were shifted to coincide with STEC calculated from the IGS GIM products.Both the Jason-2 & 3 based DORIS observations were used for the validation, several simulated +5 and -1 cycle-slip events on both DORIS observation could be clearly detected and correctly repaired. And the calculated STEC on one satellite passing arc from the LEO satellite to station show well agreement with IGS STEC on continent area, and the differences on ocean areas could be used to prove that the IGS GIM products were less precise on those areas.

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