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 | IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Shanghai, China Duration: 20 May 2019 → 24 May 2019 |
Workshop
| Workshop | IEEE International Conference on Communications Workshops, ICC Workshops 2019 |
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| Country/Territory | China |
| City | Shanghai |
| Period | 20/05/19 → 24/05/19 |
Funding
Acknowledgment: This work was funded by Business Finland in 5G Finnish Open Research Collaboration Ecosystem (5G Force) project.
Keywords
- Genetic Algorithm
- Indoor positioning
- LTE
- Neural Network
- RSS fingerprinting
- Symbolic Regression