Glonass

Sergey, Revnivykh; Bolkunov, A. I.; Serdyukov, Alexander; Montenbruck, Oliver

doi:10.1007/978-3-319-42928-1_8

Cited by 27 publications

(9 citation statements)

References 20 publications

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“…The frequency we chose to select in Table 1 to simulate GLONASS signals corresponds to the lowest frequencies of the L1 and L2 bands. Indeed, the GLONASS system uses channels of frequencies separated by about 0.5 MHz to distinguish the signals coming from the different satellites of the constellation [ 56 ].…”

Section: Methodsmentioning

confidence: 99%

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset

Panetier

Bosser

Khenchaf

2023

Sensors

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The atmospheric water vapor is commonly monitored from ground Global Navigation Satellite System (GNSS) measurements, by retrieving the tropospheric delay under the Zenith Wet Delay (ZWD) component, linked to the water vapor content in the atmosphere. In recent years, the GNSS ZWD retrieval has been performed on shipborne antennas to gather more atmospheric data above the oceans for climatology and meteorology study purposes. However, when analyzing GNSS data acquired by a moving antenna, it is more complex to decorrelate the height of the antenna and the ZWD during the Precise Point Positioning (PPP) processing. Therefore, the observation modeling and processing parametrization must be tuned. This study addresses the impact of modeling on the estimation of height and ZWD from the simulation of shipborne GNSS measurements. The GNSS simulation is based on an authors-designed simulator presented in this article. We tested different processing models (elevation cut-off angle, elevation weighting function, and random walk of ZWD) and simulation configurations (the constellations used, the sampling of measurements, the location of the antenna, etc.). According to our results, we recommend processing shipborne GNSS measurements with 3° of cut-off angle, elevation weighting function square root of sine, and an average of 5 mm·h−1/2 of random walk on ZWD, the latter being specifically adapted to mid-latitudes but which could be extended to other areas. This processing modeling will be applied in further studies to monitor the distribution of water vapor above the oceans from systematic analysis of shipborne GNSS measurements.

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Section: Methodsmentioning

confidence: 99%

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset

Panetier

Bosser

Khenchaf

2023

Sensors

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“…e most common and widely used Positioning System is the Global Navigation Satellite System (GNSS) [62][63][64], a satellite navigation system with global coverage. ere are several GNSS systems, namely, Global Navigation Satellite System (GLONASS) [65,66], Global Positioning System (GPS) [67][68][69], BeiDou Navigation Satellite System (BDS) [70], Galileo [71], Quasi-Zenith Satellite System (QZSS) [72,73], and others. GNSS positioning systems have several drawbacks.…”

Section: Positioning Systemmentioning

confidence: 99%

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Mohamed

Al-Shalfan

2021

Journal of Advanced Transportation

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The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.

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“…Radio signals are also subjected to refraction, reflection, scattering, and absorption during ionosphere transmission. This will delay global navigation satellite system (GNSS) signals and cause several hundred-meter errors, making it a rather thorny problem in GNSS data processing [1][2][3][4][5] . For dual-frequency receivers, the ionosphere can be eliminated by constructing ionospheric combined observations, which are inversely proportional to the square of signal frequency.…”

Section: Introductionmentioning

confidence: 99%

Ionospheric prediction algorithm and its application in low-latitude regions based on the physically constrained polynomial model

Huang,

Chen,

Xin

et al. 2023

J. Phys.: Conf. Ser.

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With sufficient consideration of the ionospheric variation subject to solar activity and geomagnetic variation, a high-precision ionospheric prediction model, i.e., a physically constrained polynomial model (PCPM), was constructed by adding the Kp index, Dst index, F10.7P, R sunspot number, and other geomagnetic and solar variation indexes. Based on the data of Continuously Operating Reference System (CORS) stations in China Southern Power Grid, the ionospheric prediction results of PCPM were compared with the widely used and well-recognized seasonal enhanced product autoregressive integrated moving average (Arima) model without additional physical parameters. The prediction performance of the two models in different prediction time spans and the influence of model products on Precise Point Positioning-Real Time Kinematic (PPP-RTK) was emphatically analyzed. The experimental results demonstrated that the accuracy of PCPM is better than that of the seasonal Arima model on the first day of prediction, the prediction accuracy of the two models decreases with the increase of prediction time span, and the lowest accuracy of the seasonal Arima model on the first three days is 1.5 TECU. The Arima model outperforms the PCPM over a long-time span; in light of the overall accuracy, PCPM is more available in five southern provinces, outperforming the seasonal Arima model; the results of the PPP-RTK experiment showed that the PCPM could not only improve the velocity of ambiguity fixing but also improve the positioning accuracy after ambiguity fixing. Compared with the seasonal Arima model, the velocity of ambiguity fixing is increased by 16.7%, and the positioning accuracy after ambiguity fixing is improved by 61.0% in the E direction and 6.9% in the U direction compared with the results without ionospheric constraints.

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Glonass

Cited by 27 publications

References 20 publications

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Ionospheric prediction algorithm and its application in low-latitude regions based on the physically constrained polynomial model

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