Abstract
Indoor positioning is a key technology enabler for various smart systems that require location-based optimization and automation. In this paper, we present CO-LEELM, a continuous-output location fingerprinting method that combines two existing location fingerprinting methods to produce better accuracy in a dynamic environment where training data and reference devices are sparsely-distributed. The proposed method incorporates the use of Extreme Learning Machine (ELM) to improve the training speed which is a crucial factor that affects the scalability of the method.
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Acknowledgements
This research was partially supported by the ST Engineering – NTU Corporate Lab through the NRF corporate lab@university scheme.
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Dwiyasa, F., Lim, MH. (2020). CO-LEELM: Continuous-Output Location Estimation Using Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_25
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DOI: https://doi.org/10.1007/978-3-030-23307-5_25
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Online ISBN: 978-3-030-23307-5
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