Almost all mobile devices have built-in location intelligence. Many industries have been transformed by mobile location intelligence. However, the location accuracy of the built-in solutions provided by the Global Navigation Satellite System (GNSS) chipsets in smartphones, or other mobile devices, is about 10 metres.
This level of precision has limited the potential for certain applications in connected ecosystems. For instance, vehicle-to-bicycle and vehicle-to-pedestrian safety interactions requires smartphones with position accuracy that can differentiate between lanes or approximately one metre distance.
The current approach for improving smartphone position accuracy requires access to RTCM GNSS raw observation (OSR) messages of about 2000 bytes per second from a data server. This data can help re-determine the position states for higher precision.
Such a high-rate of data exchanges to mobile phones is problematic as it will rapidly drain the mobile phone's battery. There is also a lack of hardware and software support for precise positioning algorithms in mobile phones. This is a large bottleneck for potential mobile applications in transport and personnel safety.
The project aims to demonstrate that the recently-designed RTCM space-state-presentation (SSR) standard messages of about 200 bytes per second can replace the OSR messages for the precise positioning.
A cloud-based GNSS analytics platform will be designed to process the multi-frequency data streams from multiple connected geoscience Australia GNSS stations and selected mobile devices in the South East Queensland area. This will generate precise SSR corrections messages, allowing the position states of mobile devices to be re-determined to the accuracy of sub-metre level.
Working with external partners, the project team will be able to demonstrate the benefits of the sub-metre mobile positioning results in their vehicle-to-people and vehicle-to-road hazard collision detection.
Depending on your skills and study level, you might be involved in:
- investigating unidirectional data communications for connections between mobile devices and cloud servers
- developing applications for accessing mobile phone GPS and location data
- recomputing locations with SSR corrections
- developing cloud-end data analytic platforms to generate the SSR corrections
- evaluating positioning performance of mobile devices against benchmark solutions
- developing applications that make use of precise mobile locations
- studying and demonstrating the applications of precise location information in road users and personal safety
- studying network security issues in connected safety applications.
Upon concluding our research, we expect to have created:
- research papers or theses in:
- GNSS data analytics
- precise GNSS positioning with dual band smartphones
- unidirectional communications for connected safety applications
- network security
- venerable road user safety.
- a cloud-based GNSS data analytics platform to handle data streams from multiple GNSS receivers and mobile devices
- mobile/web-based applications for mobile device unidirectional communications and positioning
- mobile applications for road user safety applications
Skills and experience
Desirable/required skills and expertise areas may include:
- computer science/data science majors with good knowledge and skills in:
- networks and communications
- network security
- server and mobile application development
- Internet of Things platforms
- spatial science majors with excellent knowledge and skills in:
- GNSS algorithms
- software development
- traffic/transport engineering majors with good knowledge and skills in:
- road safety traffic modelling
- traffic network simulations.
You may be able to apply for a research scholarship in our annual scholarship round.
Contact the supervisor for more information.