Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra‐Rapid Orbit Determination and Real‐Time Satellite Clock Offset Estimation

Yang, Xu; Wang, Qianxin; Xue, Shuqiang

doi:10.1155/2019/7579185

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…However, due to the number of stations that are part of the IGS network (as of 2021), the amount of available data represents a challenge for many processing algorithms that make use of such data. For applications such as GNSS orbit and clock determination, some studies apply a pre-processing step for the selection of the best stations in the network (Yang et al, 2019).…”

Section: Gnss Receiver Network and Signal Power Observationsmentioning

confidence: 99%

Gain Pattern Reconstruction of GPS Satellite Antennas Using a Global Receiver Network

Allende-Alba

Thoelert

Caizzone

2022

navi

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The increasing number of GNSS applications with reliability and integrity calls for the implementation and integration of monitoring systems that can be used to improve employed error and threat models (Thombre et al., 2018;Wang & Shen, 2020). For safety-critical applications, integrity protection levels are computed based on the expected characteristics of the error sources in the GNSS positioning system (Walter 2017). Aside from those related to signal propagation, the receiver, and its location, errors for system-based parameters are also included in the positioning error budget. Fundamentally, such parameters include satellite ephemeris, satellite clock errors, and signal biases.On a routine basis, most of these parameters are estimated by the service provider and delivered to the user via the broadcast navigation message (Langley et al., 2017). However, system-based irregularities in the transmitted signal characteristics that lead to errors in the positioning solution may be more difficult to observe Deutsches Zentrum für Luft-und Raumfahrt (DLR),

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Section: Gnss Receiver Network and Signal Power Observationsmentioning

confidence: 99%

Gain Pattern Reconstruction of GPS Satellite Antennas Using a Global Receiver Network

Allende-Alba

Thoelert

Caizzone

2022

navi

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“…In their study, Shah Sadman and Hossam-E-Haider [16] considered other factors in addition to the GDOP, namely signal availability level, number of visible satellites, scintillation fade depth, ionospheric delay and rainfall attenuation, to select ground station locations to develop a local satellite-based augmentation system (SBAS). Based on grid control probabilities, a method of configuring global station tracking networks was proposed by Yang et al [17] to achieve an optimal configuration with a minimum GDOP. The objectives of the optimal configuration were the rapid orbit of GNSSs and satellite clock estimation.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Deployment of Ground Tracking Stations for Low Earth Orbit Satellite Constellations Based on Evolutionary Algorithms

Kralfallah,

Wu,

Tahir

et al. 2024

Remote Sensing

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Low Earth orbit (LEO) satellite constellations have emerged as an effective alternative for the provision of high-accuracy positioning, navigation and timing (PNT) solutions which are based on high-precision orbit and clock information. Determining an orbit with high precision is dependent on the number and distribution of ground tracking stations. Therefore, it is important to investigate methodologies that can ensure the adequate observing coverage of LEO navigation constellations. In this study, an evolutionary algorithm is applied to optimize the number and deployment of ground stations for tracking LEO constellations. According to the distribution area, two schemes of study are analyzed: (a) global deployment—the ground stations are deployed throughout the globe; (b) regional deployment—a selected region is used for deployment. For global deployment, the optimization objectives are focused on the ground station and observing rate for k-heavy observing coverage (HC), while the sole objective for the regional deployment scheme is the satellite position dilution of precision (SPDOP). It is shown that a deployment of 95 ground stations is optimal for achieving 3-HC with an observing rate of 97.37% and 4-HC with an observing rate of 92.01%. For regional distribution, 15, 20 and 25 ground stations are used for three optimal configurations of SPDOP at 2.058, 1.399 and 1.330, respectively. The results are significantly enhanced using intersatellite links for SPDOP evaluation, from 2.058, 1.399 and 1.330 to 0.439, 0.422 and 0.409, with 15, 20 and 25 ground stations, respectively.

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“…However, regarding the real-time or near real-time centimeter-level services, the ultrarapid clock products should be selected and inserted into applications [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Improved Strategies for BeiDou Ultrarapid Satellites’ Clock Bias Prediction Using BDS-2 and BDS-3 Integrated Processing

Wang

2020

Mathematical Problems in Engineering

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GNSS ultrarapid clock biases are key inputs of rapid high-accuracy applications, especially for its prediction parts. With the fast development of the BeiDou system (BDS), the system performances are mainly represented by orbit and clock products. However, it is suggested that the BDS-predicted clock biases cannot meet the requirement of real-time or near real-time services. In this research, the BDS satellite-predicted ultrarapid clock bias products are optimized with three methods, namely, one-step strategy, intersatellite correlation, and variogram model, using the combined estimation of BDS-2 and BDS-3 satellites. Firstly, considering the traditional two-step strategy for modelling clock bias prediction, we take all terms (including trend and periodic terms) into one-step solution of model estimation based on the sparse modelling in machine learning. Secondly, because of the much more stable on-board atomic clock of BDS-3 satellites, the intersatellite correlations between BDS-2 and BDS-3 are utilized to enhance the solution of model coefficients. Thirdly, to further improve the model, the temporal correlations in model residuals are used to reconstruct the stochastic function obtained by variogram. In addition, to verify the proposed improved strategies, 12 schemes of BDS clock bias prediction experiments are designed and analyzed with different conditions. According to the results of predicted clock biases, it is indicted that (1) the stability of BDS-3 on-board clocks is more optimal compared with BDS-2, which can be used to strengthen the solution of the clock bias prediction model; (2) the one-step estimation of the clock bias model by sparse modelling can slightly increase the accuracy of prediction results; (3) both BDS-2- and BDS-3-predicted clock biases benefited each other by inserting the intersatellite correlations into the weight matrix, in which the accuracy of 18-hour period with one-step strategy can be improved by 28.6% and 27.2% for BDS-2 and BDS-3, respectively; and (4) after the introduction of the variogram model in updating the weight matrix, the clock bias prediction model is further corrected by 8.0% and 11.1% for BDS-2 and BDS-3. In summary, improved strategies for BDS ultrarapid satellites’ clock bias prediction using BDS-2 and BDS-3 integrated processing are meaningful for the current BDS ultrarapid satellites’ clock bias prediction products.

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Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra‐Rapid Orbit Determination and Real‐Time Satellite Clock Offset Estimation

Cited by 4 publications

References 16 publications

Gain Pattern Reconstruction of GPS Satellite Antennas Using a Global Receiver Network

Gain Pattern Reconstruction of GPS Satellite Antennas Using a Global Receiver Network

Optimizing the Deployment of Ground Tracking Stations for Low Earth Orbit Satellite Constellations Based on Evolutionary Algorithms

Improved Strategies for BeiDou Ultrarapid Satellites’ Clock Bias Prediction Using BDS-2 and BDS-3 Integrated Processing

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