Clarifying the Relationship between Quality of Global Positioning System Data and Precision of Positioning

Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users’ transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.

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Subsidence is determined in the heart of the Central Valley using Post Processed Static and Precise Point Positioning techniques

Journal of Applied Geodesy ◽

10.1515/jag-2019-0043 ◽

2020 ◽

Vol 14 (1) ◽

pp. 113-118 ◽

Cited By ~ 1

Author(s):

Y. Facio ◽

M. Berber

Keyword(s):

Data Processing ◽

Global Positioning System ◽

Precise Point Positioning ◽

Central Valley ◽

Gps Data ◽

Positioning System ◽

Online Services ◽

Global Positioning ◽

System Data ◽

Point Positioning

AbstractPost Processed Static (PPS) and Precise Point Positioning (PPP) techniques are not new; however, they have been refined over the decades. As such, today these techniques are offered online via GPS (Global Positioning System) data processing services. In this study, one Post Processed Static (OPUS) and one Precise Point Positioning (CSRS-PPP) technique is used to process 24 h GPS data for a CORS (Continuously Operating Reference Stations) station (P565) duration of year 2016. By analyzing the results sent by these two online services, subsidence is determined for the location of CORS station, P565, as 3–4 cm for the entire year of 2016. In addition, precision of these two techniques is determined as ∼2 cm. Accuracy of PPS and PPP results is 0.46 cm and 1.21 cm, respectively. Additionally, these two techniques are compared and variations between them is determined as 2.5 cm.

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Accuracy of a Global Positioning System (GPS) for Weed Mapping

Weed Technology ◽

10.1017/s0890037x00043438 ◽

1997 ◽

Vol 11 (4) ◽

pp. 782-786 ◽

Cited By ~ 12

Author(s):

Theodore M. Webster ◽

John Cardina

Keyword(s):

Measurement Error ◽

Global Positioning System ◽

Patch Size ◽

Positioning System ◽

Weed Mapping ◽

Global Positioning ◽

The Relationship

Experiments were conducted to test the accuracy of a global positioning system (GPS) in measuring the area of simulated weed patches of varying size and to determine the accuracy in navigating back to particular points in a field. Circular areas of 5, 50, and 500 m2 were established and measured using point and polygon features of a GPS. The GPS estimations of the area of those patches had errors ranging from 7 to 45%, 6 to 15%, and 3 to 6%, respectively, when compared to actual measurements. As patch size increased, errors decreased. A curve describing the relationship between GPS error and patch size had an excellent fit (r2 = 0.92). The error remained the same in all measurements across all patch sizes, but composed a smaller percentage of large patches. The GPS had submeter accuracy in navigation to the correct quadrat 73% of the time, located the correct quadrat 27% of the time, and invariably navigated to within 1.58 m of the correct quadrat. The relationship between patch size and measurement error was applied to natural infestations of hemp dogbane.

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