A Signal-Physical Siamese neural network for Wi-Fi Fingerprint localization

It is important to obtain accurate indoor location information in the Wi-Fi fingerprinting localization system, which suffers from a lot of labour-power to construct the fingerprint database. Improving positioning accuracy with less manual labour is still an issue. In this paper, we propose a signal-physical Siamese neural network SPSNN, consisting of a signal distance matrix encoder and a physical distance matrix encoder to learn features. The main idea of the SPSNN is to use the physical feature to constrain the signal feature. Grid fingerprints are constructed using crowdsourcing samples. We compute the distance difference between the sample and different grid fingerprints in signal space to build the pictures as the input of the Siamese network for further feature extraction. In addition, we exploit the distance distribution information in physical space to modify and constrain the signal feature vector during the network training phase. The position prediction is given by the regression method. Experiments on-field measurements validate the effectiveness and robustness of the proposed model, and the optimal localization accuracy is less than 1 m.