GPS for high resolution positioning
With the advent of a wide-ranging suite of applications and online services utilizing data from the Global Positioning Systems (GPS), map-matching, which is the problem of aligning noisy observed coordinates to road networks, on a given map, is now an increasingly important problem. In this project,...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/139138 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | With the advent of a wide-ranging suite of applications and online services utilizing data from the Global Positioning Systems (GPS), map-matching, which is the problem of aligning noisy observed coordinates to road networks, on a given map, is now an increasingly important problem. In this project, a Fast Hidden-Markov Model (HMM) is proposed and implemented in Python, deriving the most likely trajectory undertaken by a traveler based on observed trajectories emitted from his/her GPS. Our experiment results indicate that our model performs with a relatively high degree of accuracy on the base case and remains so even as the GPS sampling frequency is significantly reduced. Although the processing time of our algorithm is significant, it also has better performance as compared to other major open source implemented algorithms that can be found. |
|---|