A Convolutional Neural Network Architecture for Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

Nowadays Location Based Services applications are increasingly useful. However, problems like floor identification for multi-buildings and adverse effects of devices diversity are needed to be resolved. In this paper we propose a new approach using cosine similarity computed by Wi-Fi fingerprints an...

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Bibliographic Details
Main Authors: Chen, Xin, Siu, Wan-Chi, Chan, Yuk-Hee, Chan, Chuen-Yu, Chau, Chun-Pong
Format: Conference Proceeding
Language:English
Subjects:
CNN
Cosine similarity
Deep learning
Digital signal processing
Fingerprint recognition
floor classification
Location awareness
small data set
Training
Training data
Urban areas
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Summary:Nowadays Location Based Services applications are increasingly useful. However, problems like floor identification for multi-buildings and adverse effects of devices diversity are needed to be resolved. In this paper we propose a new approach using cosine similarity computed by Wi-Fi fingerprints and radio map and using Convolutional Neural Network (CNN) model to achieve multi-floor classification. We propose in this paper to use locations-based similarity as the feature vector instead of using conventional Access Point sets. We also use a timesaving walk-survey method to collect Wi-Fi fingerprint. Experimental results show that our proposed CNN floor classifier has 98.37% training accuracy and 99.51% test accuracy. Compared with recent deep neural networks, our proposed approach achieves state-of-the-art floor classification accuracy but only needs a training data set almost 5 times smaller than that of other approaches.
ISSN:2165-3577
DOI:10.1109/DSP58604.2023.10167952