GEO1003: Positioning and Location Awareness
This course addresses Global Navigation Satellite Systems (GNSS) and (indoor) positioning technologies for sensing people, devices, and assets in the built environment with the focus on location-aware applications. The course covers the requirements and context for these location-aware applications: global, local, and linear reference systems, coordinate systems and map projections, positioning methods and techniques, and the social and technical push and legislative pull factors that empower the development of location-based services.
The course consists of three parts:
- Analyses of location aware applications: concepts of location awareness, location sensitivity, and context awareness; use cases of location-based applications; technical and legislation factors, privacy issues, and standards.
- Global, local (e.g. Dutch), and linear reference systems; coordinate systems and transformations; basic types of map projections.
- Positioning methods, techniques, and performance: Global Navigation Satellite Systems; terrestrial radio-based positioning by using Bluetooth, UWB, RFID, Wi-Fi and other short range technologies; Inertial Navigation Systems (INS); presence-based locating; technologies with dead reckoning and map-matching methods; fingerprinting (templates) for indoor positioning.
Coursework Overview
GNSS Performance Measurement
Analyzed the relationship between Dilution of Precision (DOP) values and satellite visibility metrics using raw NMEA sentences from GPS/GNSS receivers. This project demonstrates understanding of how satellite geometry affects positioning accuracy.
Key Outcomes
- Quantified the relationship between satellite geometry and position uncertainty
- Demonstrated how urban canyons and obstructions affect GNSS performance
- Implemented algorithms for real-time DOP calculation and visualization
Documentation
GPS/GNSS to Local CRS using RDNAPTRANSā¢
Developed a complete coordinate transformation workflow from raw GNSS measurements to the Dutch national reference system, demonstrating proficiency in geodetic reference frame transformations.
Key Outcomes
- Created reusable coordinate transformation functions following OGC and RDNAP standards
- Validated transformation accuracy against known control points
- Documented transformation parameters and their temporal validity
Documentation
Wi-Fi Fingerprinting in Faculty Building
Implemented machine learning-based indoor positioning system using Wi-Fi signal strength measurements collected throughout the BK-City faculty building.
Key Outcomes
- Achieved sub-room level positioning accuracy
- Identified optimal fingerprint density for balancing collection effort and accuracy
- Developed methodology for handling temporal variations in Wi-Fi signal strength
- Created interactive visualization of positioning uncertainty throughout the building