Towards tropospheric delay estimation using GNSS smartphone receiver network

Marques, Tiago De Oliveira; Mäkelä, Maija; Montloin, Leslie; Lehtola, Terhi; Thombre, Sarang; Lehtola, Ville V.

doi:10.1016/j.asr.2020.09.041

Cited by 7 publications

(4 citation statements)

References 25 publications

(29 reference statements)

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“…Figure 4 In addition to data measurement, smartphones can be used as widespread pinpoint devices for conducting wide surveys and mass data collection because of their large popularity and ease of carry. An example of such usage is in meteorology where a smartphone network can be implemented to monitor tropospheric delays and variations based on the changes in data collected by smartphone sensors [27].…”

Section: Computer-aided Designmentioning

confidence: 99%

Challenges and Solutions for Engineering Applications on Smartphones

Khoury,

Kaddaha,

Saade

et al. 2023

Software

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This paper starts by presenting the concept of a mobile application. A literature review is conducted, which shows that there is still a certain lack with regard to smartphone applications in the domain of engineering as independent simulation applications and not only as extensions of smartphone tools. The challenges behind this lack are then discussed. Subsequently, three case studies of engineering applications for both smartphones and the internet are presented, alongside their solutions to the challenges presented. The first case study concerns an engineering application for systems control. The second case study focuses on an engineering application for composite materials. The third case study focuses on the finite element method and structure generation. The solutions to the presented challenges are then described through their implementation in the applications. The three case studies show a new system of thought concerning the development of engineering smartphone applications.

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Section: Computer-aided Designmentioning

confidence: 99%

Challenges and Solutions for Engineering Applications on Smartphones

Khoury,

Kaddaha,

Saade

et al. 2023

Software

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“…Few references in GNSS literature devise crowdsourcing approaches for GNSS science. De Oliveira et al (2020), proposed a crowdsourcing concept to estimate troposphere water vapour distribution from GNSS using a simulated smartphone network. However, one of the key feasibility problems is the definition of the effective minimum sample for crowdsourced data (Meng 2018).…”

Section: Raw Gnss Data Internet Of Things and Crowdsourcingmentioning

confidence: 99%

A Data-Intensive Approach to Exploit New GNSS Science Opportunities

Navarro

Ventura-Traveset

2021

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

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With the current GNSS infrastructure development plans, over 120 GNSS satellites (including European Galileo satellites)will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis.This global and permanent GNSS infrastructure constitutes a major opportunity for GNSS science applications. In themeantime, recent advances in technology have contributed "de-facto" to the deployment of a large GNSS receiver arraybased on Internet of Things (IoT), affordable smart devices easy to find in everybody’s pockets. These devices – evolvingfast at each new generation – feature an increasing number of capabilities and sensors able to collect a variety ofmeasurements, improving GNSS performance. Among these capabilities, Galileo dual band smartphones receivers andAndroid’s support for raw GNSS data recording represent major steps forward for Positioning, Navigation and Timing (PNT)data processing improvements. Information gathering from these devices, commonly referred as crowdsourcing, opensthe door to new data-intensive analysis techniques in many science domains. At this point, collaboration between variousresearch groups is essential to harness the potential hidden behind the large volumes of data generated by thiscyberinfrastructure. Cloud Computing technologies extend traditional computational boundaries, enabling execution ofprocessing components close to the data. This paradigm shift offers seamless execution of interactive algorithms andanalytics, skipping lengthy downloads and setups. The resulting scenario, defined by a GNSS Big Data repository with colocatedprocessing capabilities, sets an excellent basis for the application of Artificial Intelligence / Machine Learning (ML)technologies in the context of GNSS. This unique opportunity for science has been recognized by the European SpaceAgency (ESA) with the creation of the Navigation Scientific Office, which leverages on GNSS infrastructure to deliverinnovative solutions across multiple scientific domains.

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Section: Raw Gnss Data Internet Of Things and Crowdsourcingmentioning

confidence: 99%

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

Furones¹,

Esteban²

2021

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The use of medium/high-density LIDAR (Light Detection And Ranging) data for land modelling and DTM (Digital Terrain Model) is becoming more widespread. This level of detail is difficult to achieve with other means or materials. However, the horizontal and vertical geometric accuracy of the LIDAR points obtained, although high, is not homogeneous. Horizontally you can reach precisions around 30-50 cm, while the vertical precision is rarely greater than 10-15 cm. The result of LIDAR flights, are clouds of points very close to each other (30-60 cm) with significant elevation variations, even if the terrain is flat. And this makes the triangulated models TIN (Triangulated Irregular Network) obtained from such LIDAR data especially chaotic. Since contour lines are generated directly from such triangulated models, their appearance shows excessive noise, with excessively broken and rapidly closed on themselves. Getting smoothed contour liness, without decreasing accuracy, is a challenge for terrain model software. In addition, triangulated models obtained from LIDAR data are the basis for future slope maps of the land. And for the same reason explained in the previous paragraph, these slope maps generated from high or medium density LIDAR point clouds are especially heterogeneous. Achieving uniformity and greater adjustment to reality by reducing the natural noise of LIDAR data is another added challenge. In this paper, the problem of excessive noise from LIDAR data of high (around 8 points/m 2 ) and medium density (around 2 points/m 2 ) in the generation of contour lines and terrain slope maps is raised and solutions are proposed to reduce this noise. All this, in the area of specific software for the management of TIN models and GIS (Geographic Information System) and adapting the alternatives proposed by these programmes.

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Towards tropospheric delay estimation using GNSS smartphone receiver network

Cited by 7 publications

References 25 publications

Challenges and Solutions for Engineering Applications on Smartphones

Challenges and Solutions for Engineering Applications on Smartphones

A Data-Intensive Approach to Exploit New GNSS Science Opportunities

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

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