IFN712 Research Project Proposal-Form
IFN712 研究Project Proposal-Form
(Submitted to y.feng@qut.edu.au by 30 June 2024)
(在 2024 年 6 月 30 日之前提交至 y.feng@qut.edu.au)
Project agency (school, industry) | School of Computer Science |
Industry supervisor and contact emails | TO be confirmed |
Academic Supervisor name(s) and contact emails | Wenzong Gao, w9.gao@qut.edu.au Yanming Feng, y.feng@qut.edu.au |
Information Technology major(s) | Data Science and Computer Science |
Project title | Precise modelling and prediction for GNSS code multipath delays using machine learning approaches |
Brief description of the research problem, gaps, aims, methodology and expected outputs (~200 words) | The Global Navigation Satellite Systems (GNSS), particularly the US's Global Positioning System (GPS), have become integral to daily life, industry, military operations, and scientific research. GNSS technology allows for the precise determination of any receiver's 3D coordinates on Earth's surface, provided the receiver has line-of-sight (LOS) measurements, i.e., receiver-to-satellite distances, to at least four satellites. Using the known positions of these satellites and the measured receiver-to-satellite distances, the receiver’s position can be accurately calculated. The measurement of the receiver-to-satellite distance depends on the time difference between the signal transmitted by the satellite and received by the receiver. However, various factors can introduce errors into these measurements. For instance, atmospheric delays occur when signals are distorted as they pass through the atmosphere. Additionally, multipath delays, which happen when satellite signals are reflected and diffracted by tall buildings and skyscrapers, often reduce accuracy by several tens of meters. While most errors can be corrected using mathematical or empirical models, multipath errors are challenging to model through traditional methods, making them the most significant source of GNSS measurement errors. This project aims to apply Machine Learning (ML) techniques to model code multipath delays in GNSS code measurements. These delays will be calculated from dual-frequency GNSS measurements, and variables related to the multipath delays will be selected as inputs for training an ML model. The model will predict multipath delays, which can then be used to correct incoming GNSS measurements, thereby improving positioning accuracy. Our objective is to identify optimal configurations for ML models that deliver precise predicted code multipath delay corrections, thus enhancing GNSS positioning accuracy. |
Answerable research questions for 3-5 students | What are the traditional methods for correcting multipath effects? How to obtain the multipath delay time series from dual-frequency GNSS measurements? What variables are related to the multipath delays and can be used as inputs for training the machine learning models? How the machine learning models’ performance varies when using different input variables combinations? What modelling and prediction precisions can be achieved through the trained machine learning models What positioning performance improvements (measured by accuracy or Precise Point Positioning [PPP] convergence time) can be achieved by introducing multipath corrections generated by machine learning models? |
3-5 key references (very preferable for students to start) | Braasch, M. S. (2017). Multipath. In (pp. 443-468). Springer International Publishing. https://doi.org/10.1007/978-3-319-42928-1_15 Strode, P. R. R., & Groves, P. D. (2016). GNSS multipath detection using three-frequency signal-to-noise measurements. GPS solutions, 20(3), 399-412. https://doi.org/10.1007/s10291-015-0449-1 Li, Q., Xia, L., Chan, T. O., Xia, J., Geng, J., Zhu, H., & Cai, Y. (2020). Intrinsic Identification and Mitigation of Multipath for Enhanced GNSS Positioning. Sensors, 21(1), 188. https://doi.org/10.3390/s21010188 Bilich, A., Larson, K. M., & Axelrad, P. (2008). Modeling GPS phase multipath with SNR: Case study from the Salar de Uyuni, Boliva. Journal of Geophysical Research, 113(B4). https://doi.org/10.1029/2007jb005194 Smyrnaios, M., Schn, S., Liso, M., & Jin, S. (2013). Multipath propagation, characterization and modeling in GNSS. Geodetic sciences-observations, modeling and applications, 99-125. |
Required major of studies, skills, knowledge, and speciality | Students majoring data science and computer science can participate in the project. Programming skills (Python or Matlab) |
Industry-based project: Student IP Agreement. This is the IP model agreed between the parties. Please note that it is QUT policy that where possible students should be allowed to keep their IP. If students are asked to assign their work then please provide a brief rationale as additional permissions are needed by QUT to approve. | ☐ Project IP vests in the Student with a license back to Industry Partner (licence) OR ☐ Project IP vests in the Industry Partner with a licence back to the Student (assignment) OR ☒ Academic project |
Number of students | 3-5 |
Student names (if known) | |
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Remarks on conditions of offer | The supervising team will shortlist the candidates after their application. |