Analyzing Android GNSS Raw Measurements Flags Detection Mechanisms for Collaborative Positioning in Urban Environment

Verheyde, Thomas; Blais, Antoine; Macabiau, Christophe; Marmet, François-Xavier

doi:10.1109/icl-gnss49876.2020.9115564

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…As a part of our urban analysis, we conducted a study on those flags' detection mechanisms. The results provided in [7] reveal that no raw data measurements can be directly correlated to the output value of those flags. A more complex algorithm is then responsible for determining cycle slip and multipath events.…”

Section: Ii3 -Urban Environmentmentioning

confidence: 99%

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An Assessment Methodology of Smartphones Positioning Performance for Collaborative Scenarios in Urban Environment

Verheyde¹,

Blais²,

Macabiau³

et al. 2020

Proceedings of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 202

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Thomas VERHEYDE graduated from ENAC ("The French civil aviation university") with a Master of Science in Aerospace Systems Navigation & Telecommunication. Currently pursuing my PhD in the GNSS domain, my thesis' title is "Precise Cooperative Positioning of Low-Cost Mobiles in an Urban Environment". Employed by TéSA (Telecommunication Research Laboratory) in Toulouse and my research is co-funded by CNES (French space agency) and Thalès Alénia Space.Dr. Antoine BLAIS has earned an engineering degree from ENAC (École Nationale de l'Aviation Civile,

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Section: Ii3 -Urban Environmentmentioning

confidence: 99%

“…A na ly sis of th e C MC f o r G a lile o PRN 12 E1 f o r a X ia o mi M i8 in u r ba n co nst r a int s[ 7 ].…”

mentioning

confidence: 99%

An Assessment Methodology of Smartphones Positioning Performance for Collaborative Scenarios in Urban Environment

Verheyde¹,

Blais²,

Macabiau³

et al. 2020

Proceedings of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 202

Self Cite

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“…Moreover, Android provides a certain amount of flags on code and phase tracking quality, as well as multipath flags, directly within the API. These flags have not been used in this analysis but should be checked for more advanced positioning computations, as assessed in previous studies [48,49].…”

Section: Measurements Analysismentioning

confidence: 99%

Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

Grenier,

Lohan,

Ometov

et al. 2023

Electronics

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The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges.

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“…Verheyde et al tried to find answer for it. They concluded that the computation processes used in these mechanisms do not solely rely on naive interpolation of C/N0 (Carrier-to-Noise Density) or satellite elevation parameters [28]. The similarities observed across different smartphone brands suggest that these detection algorithms might be implemented at a low-level Android layer.…”

Section: Introductionmentioning

confidence: 99%

Optimize Kalman Filter For Smart Phone Location Tracking Using GNSS Measurements

Yokanathan

2023

Int J on Adv. in ICT for Emerging Countries

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Point precise positioning (PPP) method allows to locate user with Global Navigation Satellite System under good condition. But this method is quite challenging to locate user when user is in urban areas with obstacles. Another alternative solutions such as Real Time Kinematic solve these problems with differential measurements. In real world scenario, raw GNSS measurements have usually noisy and uncertain data. Kalman filter is successful method to optimally update variables with indirect measurements. This research proposes the method to optimize Kalman filter based PPP to improve smart phone location tracking. Evaluation method showed that proposed method worked well with approximately 3.0 meter accuracy and outperformed base line model which is calculated using Weighted-Least-Square (WLS) solver.

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Analyzing Android GNSS Raw Measurements Flags Detection Mechanisms for Collaborative Positioning in Urban Environment

Cited by 7 publications

References 3 publications

An Assessment Methodology of Smartphones Positioning Performance for Collaborative Scenarios in Urban Environment

An Assessment Methodology of Smartphones Positioning Performance for Collaborative Scenarios in Urban Environment

Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

Optimize Kalman Filter For Smart Phone Location Tracking Using GNSS Measurements

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