Abstract
We study the performance of a composite positioning method based on Genetic Algorithms (GA) for determining the position of an indoor user using Radio Signal Strength (RSS) fingerprinting. RSS samples are obtained from field measurements in a test-bed with a dedicated LTE network. Radio maps are described by functions obtained by symbolic regression. The position of a user is then obtained by solving an optimization problem with the functions and instantaneous measured RSS values. The proposed method is benchmarked against a solution implemented with a Neural Network. Position accuracy is evaluated in scenarios with different characteristics. Conclusions are drawn about performance and complexity of the assessed methods.
Original language | English |
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Title of host publication | 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-2373-8 |
DOIs | |
Publication status | Published - May 2019 |
MoE publication type | A4 Article in a conference publication |
Event | 2019 IEEE International Conference on Communications Workshops: ICC Workshops 2019 - Shanghai, China Duration: 20 May 2019 → 24 May 2019 |
Workshop
Workshop | 2019 IEEE International Conference on Communications Workshops |
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Country/Territory | China |
City | Shanghai |
Period | 20/05/19 → 24/05/19 |
Keywords
- Genetic Algorithm
- Indoor positioning
- LTE
- Neural Network
- RSS fingerprinting
- Symbolic Regression