How can an orbit prediction module speed up the TTFF and help to authenticate the position?

Lytvyn, M. O.; Kemetinger, Albert; Berglez, Philipp

doi:10.1109/navitec.2012.6423124

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…In order to compute orbits predictions, the UE has to integrate satellite motions by modeling all forces acting on GNSS satellites [74]. Although this process can be power consuming, self-assistance can reduce the TTFF by a factor of up to 5 times [75]. More recent studies show how neural networks are able to more accurately predict orbits with less computational effort [76].…”

Section: G Ttffmentioning

confidence: 99%

A Survey on IoT Positioning Leveraging LPWAN, GNSS, and LEO-PNT

Janssen

Koppert²,

Berkvens

et al. 2023

IEEE Internet Things J.

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Location data is an important piece of information in many Internet of Things (IoT) applications. Global Navigation Satellite Systems (GNSS) have been established as the standard for large-scale localization. However, the rapidly increasing need to locate IoT devices in recent years has exposed several shortcomings of traditional GNSS approaches. These limitations include the weak signal propagation in indoor and dense environments, the inability to calculate or obtain a location remotely, and a high energy consumption. Therefore, several industries have shown an increasing demand for alternative and innovative positioning solutions that are more suited in an IoT context. Hence, we conduct a survey on state-of-the-art, large-scale and energy-efficient positioning techniques for IoT applications. More specifically, we analyze the performance of terrestrial-based Low Power Wide Area Network (LPWAN) techniques, novel GNSS solutions, and innovative positioning techniques leveraging Low Earth Orbit (LEO) satellite constellations. A comparison is made in terms of 16 dimensions including energy consumption, positioning accuracy, coverage, and scalability. The analysis shows that interoperability between technologies is key to enable energy-efficient communication and positioning applications in the emerging market of satellite IoT.

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Section: G Ttffmentioning

confidence: 99%

A Survey on IoT Positioning Leveraging LPWAN, GNSS, and LEO-PNT

Janssen

Koppert²,

Berkvens

et al. 2023

IEEE Internet Things J.

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“…The force model considered in this work includes four major forces: the Earth, Sun and Moon gravity, and solar radiation pressure (SRP). Then the equation of motion (1) can be written as (2) We use EMG2008 as our Earth gravity model, taking terms up to degree and order 8; the geo-potential coefficients are published by National Geospatial-Intelligence Agency [5]. Moon and Sun are modeled as point masses.…”

Section: Force Modelmentioning

confidence: 99%

“…This technology is especially useful as an alternative to positioning assistance data obtained through a network connection [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Application of the Hill-Clohessy-Wiltshire equation in GNSS orbit prediction

Zhang

Piché

2014

International Conference on Localization and GNSS 2014 (ICL-GNSS 2014)

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We present an initial state correction algorithm for GNSS orbit prediction that uses the solution of the HilI-Clohessy Wiltshire equation to build the Jacobian matrix. This algorithm improves the accuracy of GNSS orbit prediction through adding one or more observations. In the tests of 14 days orbit prediction, by adding one broadcast ephemeris as an observation collected before the initial BE with a long enough interval, we get orbit only User Range Error within 70 meters, and orbit errors in the radial direction is less than 15 meters. In tangential direction, the error is within 400 meters.

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“…As mentioned above, the main reason for the long TTFF of the receiver is that the broadcast ephemeris collection is a rather time-consuming process [8]. A widely used alternative to broadcast ephemeris collection is calculation of the satellite position using auxiliary data from a data server through a terrestrial network.…”

Section: Introductionmentioning

confidence: 99%

Assisted cold start method for GPS receiver with artificial neural network-based satellite orbit prediction

Yang¹,

Zhang²,

Chen³

et al. 2020

Meas. Sci. Technol.

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Traditional GPS receivers usually take a long time to obtain their first position solution in the cold start mode. This paper presents a method for assisting the cold start of GPS receivers by autonomous satellite orbit prediction. In this method, the time-consuming broadcast ephemeris collection process in a traditional cold start is replaced by orbit prediction to obtain the satellite position information required for positioning. Thus, the time to first fix of the receiver can be reduced significantly. In addition, since the traditional orbit prediction algorithms based solely on physics-based models have limited accuracy, a hybrid model composed of dynamic models and artificial neural network models for orbit prediction is proposed to improve the accuracy of the orbit prediction. Taking GPS PRN1 as an example, the simulation results show that, in 75% of cases, the satellite position error after 7 days’ prediction with the dynamic models is less than 162.84 m, but this value can be reduced to 46.06 m with the proposed hybrid model; the improvement rate achieved is approximately 71.71%. Some positioning experiments with simulated satellite signals were conducted to assess the first-fix accuracy of a GPS receiver with the proposed satellite orbit prediction function. The results of this study offer insights for the design of advanced assisted GPS receivers.

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How can an orbit prediction module speed up the TTFF and help to authenticate the position?

Cited by 5 publications

References 10 publications

A Survey on IoT Positioning Leveraging LPWAN, GNSS, and LEO-PNT

A Survey on IoT Positioning Leveraging LPWAN, GNSS, and LEO-PNT

Application of the Hill-Clohessy-Wiltshire equation in GNSS orbit prediction

Assisted cold start method for GPS receiver with artificial neural network-based satellite orbit prediction

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