Starting from 2016,the raw Global Navigation Satellite System(GNSS)measurements can be extracted from the Android Nougat(or later)operating systems.Since then,GNSS smartphone positioning has been given much attention....Starting from 2016,the raw Global Navigation Satellite System(GNSS)measurements can be extracted from the Android Nougat(or later)operating systems.Since then,GNSS smartphone positioning has been given much attention.A high number of related publications indicates the importance of the research in this field,as it has been doing in recent years.Due to the cost-effectiveness of the GNSS smartphones,they can be employed in a wide variety of applications such as cadastral surveys,mapping surveying applications,vehicle and pedestrian navigation and etc.However,there are still some challenges regarding the noisy smartphone GNSS observations,the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning.In this review paper,we overview the research works carried out in this field with a focus on the following aspects:first,to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4,Google Pixel 5,Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities,second,to describe the current state of smartphone positioning research field until most recently in 2021 and,last,to summarize major challenges and opportunities in this filed.Finally,the paper is concluded with some remarks as well as future research perspectives.展开更多
Smartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. Th...Smartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (hand-held, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments. In such environments, the system suffers from frequent phase loss-of-lock leading to data gaps. The smartphone measurements have low and irregular carrier-to-noise (C/N0) density ratio and high multipath, which leads to poor or no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine. Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS) error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were 20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic scenario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solu-tion as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5-10% percentage point more availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged file cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution. The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Elec-tronics Association) file was computed using point positioning with the aid of measurements from internal sensors. The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous computation of solution.展开更多
基金Natural Sciences and Engineering Research Council of Canada(NSERC).
文摘Starting from 2016,the raw Global Navigation Satellite System(GNSS)measurements can be extracted from the Android Nougat(or later)operating systems.Since then,GNSS smartphone positioning has been given much attention.A high number of related publications indicates the importance of the research in this field,as it has been doing in recent years.Due to the cost-effectiveness of the GNSS smartphones,they can be employed in a wide variety of applications such as cadastral surveys,mapping surveying applications,vehicle and pedestrian navigation and etc.However,there are still some challenges regarding the noisy smartphone GNSS observations,the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning.In this review paper,we overview the research works carried out in this field with a focus on the following aspects:first,to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4,Google Pixel 5,Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities,second,to describe the current state of smartphone positioning research field until most recently in 2021 and,last,to summarize major challenges and opportunities in this filed.Finally,the paper is concluded with some remarks as well as future research perspectives.
基金Natural Sciences and Engineering Research Council of Canada(NSERC).
文摘Smartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (hand-held, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments. In such environments, the system suffers from frequent phase loss-of-lock leading to data gaps. The smartphone measurements have low and irregular carrier-to-noise (C/N0) density ratio and high multipath, which leads to poor or no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine. Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS) error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were 20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic scenario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solu-tion as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5-10% percentage point more availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged file cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution. The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Elec-tronics Association) file was computed using point positioning with the aid of measurements from internal sensors. The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous computation of solution.
