FLAMINGO – Fulfilling enhanced location accuracy in the mass-market through initial GalileO services

This paper discusses FLAMINGO, an initiative that will provide a high accuracy positioning service to be used by mass market applications. The status and future for the initiative are discussed, the required accuracies and other location parameters are described, and the target applications are identified. Finally, the currently achieved accuracies from today’s Smartphones are assessed and presented. FLAMINGO (Fulfilling enhanced Location Accuracy in the Mass-market through Initial GalileO services), part funded through the European GNSS Agency, is a collaborative venture comprising NSL (as lead organization), Telespazio France, University of Nottingham, Rokubun, Thales Alenia Space France, VVA, BQ, ECLEXYS and Blue Dot Solutions. The initiative is developing the infrastructure, solutions and services to enable the use of accurate and precise GNSS within the mass-market, thereby operating predominantly in an urban environment. Whilst mass-market receivers are yet to achieve accuracies below one metre for standard positioning, the introduction of Android raw GNSS measurements and the Broadcom dual frequency chipset (BCM47755), has presented the devices such an opportunity. FLAMINGO will enable and demonstrate the future of high accuracy positioning and navigation information on mass-market devices such as smartphones and Internet of Things (IoT) devices by producing a service delivering accuracies of 50cm (at 95%) and better, employing multi-constellation, PPP and RTK mechanisms, power consumption optimisation techniques. Whereas the Galileo High Accuracy Service targets 10cm precision within professional markets, FLAMINGO targets 30-50cm precision in the mass-market consumer markets. By targeting accuracies of a few decimetres, a range of improved and new applications in diverse market sectors are introduced. These sectors include, but are not limited to, mapping and GIS, autonomous vehicles, AR environments, mobile-location based gaming and people tracking. To obtain such high accuracies with mass market devices, FLAMINGO must overcome several challenges which are technical, operational and environmental. This includes the hardware capabilities of most mass-market devices, where components such as antennas and processors are prioritised for other purposes. We demonstrate that, despite these challenges, FLAMINGO has the potential to meet the accuracy required. Tests with the current Smartphones that provide access to multi-constellation raw measurements (the dual frequency Xiaomi Mi 8 and single frequency Samsung S8 and Huawei P10) demonstrate significant improvements to the PVT solution when processing using both RTK and PPP techniques

Precise Point Positioning Augmentation for Various Grades of Global Navigation Satellite System Hardware

The next generation of low-cost, dual-frequency, multi-constellation GNSS receivers, boards, chips and antennas are now quickly entering the market, offering to disrupt portions of the precise GNSS positioning industry with much lower cost hardware and promising to provide precise positioning to a wide range of consumers. The presented work provides a timely, novel and thorough investigation into the positioning performance promise. A systematic and rigorous set of experiments has been carried-out, collecting measurements from a wide array of low-cost, dual-frequency, multi-constellation GNSS boards, chips and antennas introduced in late 2018 and early 2019. These sensors range from dual-frequency, multi-constellation chips in smartphones to stand-alone chips and boards. In order to be comprehensive and realistic, these experiments were conducted in a number of static and kinematic benign, typical, suburban and urban environments. In terms of processing raw measurements from these sensors, the Precise Point Positioning (PPP) GNSS measurement processing mode was used. PPP has become the defacto GNSS positioning and navigation technique for scientific and engineering applications that require dm- to cm-level positioning in remote areas with few obstructions and provides for very efficient worldwide, wide-array augmentation corrections. To enhance solution accuracy, novel contributions were made through atmospheric constraints and the use of dual- and triple-frequency measurements to significantly reduce PPP convergence period. Applying PPP correction augmentations to smartphones and recently released low-cost equipment, novel analyses were made with significantly improved solution accuracy. Significant customization to the York-PPP GNSS measurement processing engine was necessary, especially in the quality control and residual analysis functions, in order to successfully process these datasets. Results for new smartphone sensors show positioning performance is typically at the few dm-level with a convergence period of approximately 40 minutes, which is 1 to 2 orders of magnitude better than standard point positioning. The GNSS chips and boards combined with higher-quality antennas produce positioning performance approaching geodetic quality. Under ideal conditions, carrier-phase ambiguities are resolvable. The results presented show a novel perspective and are very promising for the use of PPP (as well as RTK) in next-generation GNSS sensors for various application in smartphones, autonomous vehicles, Internet of things (IoT), etc

Real-time geophysical applications with Android GNSS raw measurements

The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario-few mm/s for the horizontal components and around 1 cm/s for the vertical component-underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring

Positional Accuracy in Smart-Phones and Its Effect on LBS Applications.

Location-based services (LBS) highly rely on the location of the mobile user in order to provide the service tailored to that location. This location is calculated differently depending on the technology available in the used mobile device. No matter which technology is used, the location will never be calculated 100% correctly; instead there will always be a margin of error generated during the calculation, which is referred to as positional accuracy. This research has reviewed the eight most common positioning technologies available in the major current smart-phones and assessed their positional accuracy with respect to its usage by LBS applications. Given the vast majority of these applications, this research classified them into thirteen categories, and these categories were also classified depending on their level criticality as low, medium, or high critical, and whether they function indoor or outdoor. The accuracies of different positioning technologies are compared to these two criteria. Low critical outdoor and high critical indoor applications were found technologically covered; high and medium critical outdoor ones weren?t fully resolved. Finally three potential solutions are suggested to be implemented in future smartphones to resolve this technological gap: Real-Time Kinematics Global Positioning System (RTK GPS), terrestrial transmitters, and combination of Wireless Sensors Network and Radio Frequency Identification (WSN-RFID)

An analysis of multi-GNSS observations tracked by recent Android smartphones and smartphone-only relative positioning results

In this study we assess the quality of multi-GNSS observations of recent Android smartphones. The results reveal a significant drop of smartphone carrier-to-noise density ratio (C/N0) with respect to geodetic receivers, and discernible differences among constellations and frequency bands. We show that the higher the elevation of the satellite, the larger discrepancy in C/N0 between the geodetic receivers and smartphones. Thus we show that a C/N0 weighting scheme may be superior to the elevation dependent one usually adopted for GNSS observations. We also discover that smartphone code pseudoranges are noisier by about one order of magnitude as compared to geodetic receivers, and that the code signals on L5 and E5a outperform those on L1 and E1, respectively. It is shown that smartphone phase observations are contaminated by the effects that can destroy the integer property and time-constancy of the ambiguities. There are long term drifts detected for GPS L5, Galileo E1, E5a and BDS B1 phase observations of Huawei P30. We highlight competitive phase noise characteristics for the Xiaomi Mi 8 when compared to the geodetic receivers. We also reveal a poor quality of other than GPS L1 phase signals for the Huawei P30 smartphones related to the unexpected drifts of the observations. Finally, the positioning experiment proves that it is feasible to obtain a precise cm-level solution of a smartphone to smartphone relative positioning with fixed integer ambiguities

Towards a plug&play solution for real-time precise positioning on mass-market devices

Despite pedestrian and vehicle navigation are the key applications enabled by the development of GNSS technology, the best approach to obtain accurate, reliable, continuous and robust PVT (Position-Velocity-Timing) solutions for this purpose has yet to be identified. The real limiting factor is the environment in which the users usually navigate: e.g. multipath effects and cycle slips in harsh urban environments strongly affect, respectively, pseudorange measurements and the continuity of carrier-phase observations. Therefore, positioning services relying on code-based algorithms cannot always meet the required accuracy - which varies depending on the targeted use case -; on the other hand, phase-based approaches as Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) require strong effort to deal with the ambiguity term and its reinitialization when cycle slips occur. These problems are amplified when GNSS measurements from Android smartphone are considered due to the low-cost, linearly polarized and multi-purpose antenna which inevitably impacts on the quality of GNSS observables. This paper focuses on the performance analysis of GNSS POWER - an algorithm based on the loosely coupling between Single Point Positioning (SPP) solutions and variometric velocity - combined with IGS SSR corrections to increase the accuracy achievable in a real-time stand-alone solution. The integration of SSR corrections within GNSS POWER algorithm is validated in both static and kinematic scenarios using high-end GNSS receivers and Andorid smartphones. The results demonstrated the advantages of using SSR corrections on SPP and GNSS POWER solutions also on Android devices opening to new applications of real-time stand-alone positioning approaches on mass-market devices

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT

Mass-Market Receiver for Static Positioning: Tests and Statistical Analyses

Nowadays, there are several low cost GPS receivers able to provide both pseudorange and carrier phase measurements in the L1band, that allow to have good realtime performances in outdoor condition. The present paper describes a set of dedicated tests in order to evaluate the positioning accuracy in static conditions. The quality of the pseudorange and the carrier phase measurements let hope for interesting results. The use of such kind of receiver could be extended to a large number of professional applications, like engineering fields: survey, georeferencing, monitoring, cadastral mapping and cadastral road. In this work, the receivers performance is verified considering a single frequency solution trying to fix the phase ambiguity, when possible. Different solutions are defined: code, float and fix solutions. In order to solve the phase ambiguities different methods are considered. Each test performed is statistically analyzed, highlighting the effects of different factors on precision and accurac

Automotive applications of high precision GNSS

This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas