Abstract
Indoor localisation is the state-of-the-art to identify and observe a moving human or object inside a building. Location Fingerprinting is a cost-effective software-based solution utilising the built-in wireless signal of the building to estimate the most probable position of a real-time signal data. In this paper, we apply the Conformal Prediction (CP) algorithm to further enhance the Fingerprinting method. We design a new nonconformity measure with the Weighted K-nearest neighbours (W-KNN) as the underlying algorithm. Empirical results show good performance of the CP algorithm.
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Nguyen, K., Luo, Z. (2012). Conformal Prediction for Indoor Localisation with Fingerprinting Method. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., Sioutas, S. (eds) Artificial Intelligence Applications and Innovations. AIAI 2012. IFIP Advances in Information and Communication Technology, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33412-2_22
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DOI: https://doi.org/10.1007/978-3-642-33412-2_22
Publisher Name: Springer, Berlin, Heidelberg
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