Assessing Galileo Readiness in Android Devices Using Raw Measurements

Navarro-Gallardo, M.; Bernhardt, Nils; Kirchner, Michael; Musial, Justyna Redenkiewicz; Sunkevic, Martin

doi:10.33012/2017.15183

Cited by 11 publications

(7 citation statements)

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“…Precise positioning with Android devices has also been assessed by Gill et al (2017), Navarro-Gallardo et al (2017, Pirazzi et al (2017), and Zhang et al (2018). These studies draw similar conclusions about positioning performance and raw data characteristics as the above-mentioned studies.…”

Section: Introductionsupporting

confidence: 54%

Characterization of GNSS observations from a Nexus 9 Android tablet

Håkansson

2018

GPS Solut

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Global navigation satellite system (GNSS) raw data were made available in the application programming interface (API) starting from version 7.0 of the Android operating system. This opens possibilities for precise positioning with Android devices, as externally generated GNSS corrections can now be included in the positioning estimation in a convenient way. The Nexus 9 tablet is a good candidate for an early assessment of the raw GNSS observables and the corresponding derived precise positions, as it also supports many of the optional features and observation types presented by the API, including carrier phase observations which play an important role in many precise positioning techniques. It is known from the previous studies that poor handling of multipath in smartphones and tablets is a big challenge when it comes to precise GNSS positioning with these kinds of devices. Hence, this study assesses the raw GNSS observations and the calculated precise positions of the Nexus 9 tablet in two experimental setups with different multipath impacts. In addition, various biases of the observations are determined, some of which is not present on high-grade geodetic receivers. The analysis is done for GPS and GLONASS, which are supported by the Nexus 9 tablet. The study shows that multipath plays an important role for the expected accuracy of the calculated precise positions, both due to the induced error on the measurements, and due to loss of lock of the GNSS signals, which significantly affects precise positioning from carrier-phase measurements. Position accuracy ranges from just below 1 m to a few decimeters between the experimental setups with moderate and low levels of multipath respectively, for positioning based on carrier-phase observations. It is, furthermore, demonstrated that consideration of code inter-system biases and code inter-frequency biases of the Nexus 9 tablet are crucial for differential GNSS with multiple GNSS systems and when GLONASS observations are used in the positioning solutions. Besides these expected biases, also other less expected behaviors were discovered in the Nexus 9 GNSS observations, including various drifts for the code and phase observations. This study also proposes some strategies to handle these.

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Section: Introductionsupporting

confidence: 54%

Characterization of GNSS observations from a Nexus 9 Android tablet

Håkansson

2018

GPS Solut

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“…Precise position information available from smartphones is of great importance when enabling many smartphone-based location-based service (LBS) applications. Since the GNSS observations, including carrier-phase, became available to users from smart devices running Android Nougat (version 7.0) in 2016, many methods and algorithms have been developed to enable precise positioning using these mass-market devices, such as analysis of GNSS smartphone observations [ 1 , 2 , 3 , 4 , 5 ], PPP smartphone positioning [ 6 , 7 , 8 , 9 , 10 ], real-time kinematic (RTK) smartphone positioning [ 11 , 12 , 13 , 14 , 15 ] and GNSS/INS integration using smartphone observations [ 16 , 17 , 18 ]. A comprehensive review of the recent advances and research done in the field of GNSS smartphone positioning, including those published up until 2021, can be found in [ 19 , 20 ], while some more recent contributions in 2022 are provided in the following.…”

Section: Introductionmentioning

confidence: 99%

GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement

Zangeneh-Nejad

Yang

Gao

2023

Sensors

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Precise position information available from smartphones can play an important role in developing new location-based service (LBS) applications. Starting from 2016, and after the release of Nougat version (Version 7) by Google, developers have had access to the GNSS raw measurements through the new application programming interface (API), namely android.location (API level 24). However, the new API does not provide the typical GNSS observations directly (e.g., pseudorange, carrier-phase and Doppler observations) which have to be generated by the users themselves. Although several Apps have been developed for the GNSS observations generation, various data analyses indicate quality concerns, from biases to observation inconsistency in the generated GNSS observations output from those Apps. The quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this study, we first investigate algorithms for GNSS observations generation from the android.location API output. We then evaluate the performances of two widely used Apps (Geo++RINEX logger and GnssLogger Apps), as well as our newly developed one (namely UofC CSV2RINEX tool) which converts the CSV file to a Receiver INdependent Exchange (RINEX) file. Positioning performance analysis is also provided which indicates improved positioning accuracy using our newly developed tool. Future work finding out the potential reasons for the identified misbehavior in the generated GNSS observations is recommended; it will require a joint effort with the App developers.

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“…Such a problem occurred with GNSS chipsets built in mobile phones, such as the Xiaomi Mi 8 [7][8][9]. The duty cycling is a process where the GNSS chipset of the smartphone works in a discontinuous way, which causes the hardware clock to be active only for a fraction of each second to support low power consumption and thus prevent battery discharge [10][11][12][13]. This action limited the use of smartphone observations for precise positioning techniques such as RTK and PPP.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Static Positioning Accuracy Using Low-Cost Smartphone GPS Devices for Geodetic Survey Points’ Determination and Monitoring

Uradziński

Bakuła

2020

Applied Sciences

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Recent developments enable to access raw Global Navigation Satellite System (GNSS) measurements of mobile phones. Initially, researchers using signals gathered by mobile phones for high accuracy surveying were not successful in ambiguity fixing. Nowadays, GNSS chips, which are built in the latest smartphones, deliver code and primarily carrier phase observations available for detailed analysis in post-processing applications. Therefore, we decided to check the performance of carrier phase ambiguity fixing and positioning accuracy results of the latest Huawei P30 pro smartphone equipped with a dual-frequency GNSS receiver. We collected 3 h of raw static data in separate sessions at a known point location. For two sessions, the mobile phone was mounted vertically and for the third one—horizontally. At the same time, a high-class geodetic receiver was used for L1 and L5 signal comparison purposes. The carrier phase measurements were processed using commercial post-processing software with reference to the closest base station observations located 4 km away. Additionally, 1 h sessions were divided into 10, 15, 20 and 30 min separate sub-sessions to check the accuracy of the surveying results in fast static mode. According to the post-processing results, we were able to fix all L1 ambiguities based on Global Positioning System (GPS)-only satellite constellation. In comparison to the fixed reference point position, all three 1 h static session results were at centimeters level of accuracy (1–4 cm). For fast static surveying mode, the best results were obtained for 20 and 30 min sessions, where average accuracy was also at centimeters level.

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Assessing Galileo Readiness in Android Devices Using Raw Measurements

Cited by 11 publications

References 0 publications

Characterization of GNSS observations from a Nexus 9 Android tablet

Characterization of GNSS observations from a Nexus 9 Android tablet

GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement

Assessment of Static Positioning Accuracy Using Low-Cost Smartphone GPS Devices for Geodetic Survey Points’ Determination and Monitoring

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