Abstract
With access to the raw data collected by certain Android smartphones, it is possible to perform post-processing of the data. Thus, it is possible to employ certain satellite positioning methods that were previously restricted to geodetic receivers. Thanks to this and other innovations, such as the emergence of smartphones with modern GNSS sensors, a promising scenario is seen when employing these devices in engineering applications. Generally, in certain applications that require high accuracy, centimeter and millimeter order, geodetic receivers are used. However, these devices are expensive when compared to smartphones. In this research, the coordinates of a point were determined via a smartphone with a modern GNSS sensor, whose data were post-processed by the IBGE-PPP service, using the combination GPS+GLONASS and L1 frequency. Thus, using circle adjustment techniques based on least squares, it was possible to obtain horizontal accuracy of approximately 12 cm and 25 cm with a set of about 128-hour and 24-hour sessions respectively. The results obtained in this research suggest that the applied methodology can be used in certain applications in engineering, such as land surveying of rural properties.
Highlights
Several mobile and wearable electronic devices, such as smartphones, tablets and smartwatches, use position information from Global Navigation Satellite Systems (GNSS)
According to GSA (2019), among the 1.6 billion GNSS receiver shipments in mass market devices in 2019, 90% were inserted in smartphones and wearables
All adjustments made were accepted in the hypothesis test at a significance level of 5%
Summary
Several mobile and wearable electronic devices, such as smartphones, tablets and smartwatches, use position information from Global Navigation Satellite Systems (GNSS). According to GSA (2019), among the 1.6 billion GNSS receiver shipments in mass market devices in 2019, 90% were inserted in smartphones and wearables. Under good visibility conditions, positioning via smartphones can result in accuracy better than 10 m (Banville and Diggelen, 2016), between 1 to 2 m (Kaleev and Saburova, 2018), and between 2 to 3 m (Pesyna et al, 2014). In adverse conditions, when there are obstructions, the accuracy can be worse than 10 m This level of accuracy, may be sufficient for certain LBS’s, such as navigation, vehicle tracking, social networks, deliveries, among others (Banville and Diggelen, 2016)
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