A WiFi Indoor Localization Method Based on Dilated CNN and Support Vector Regression

A novel method is proposed to improve positioning real-time property while ensuring the accuracy. Firstly, a dilated convolutional neural network (D-CNN) model is trained with images formed by the received signal strength (RSS). Secondly, the errors of the predicted results of D-CNN are used to train a support vector regression (SVR) model. Experiments are conducted using the public database collected from a library of Universitat Jaume I in Spain. The results demonstrated the superior performance of D-CNN. Moreover, the results proved that the average runtime of the proposed D-CNN + SVR algorithm was only 0.612s, which was reduced by 86.27% compared with P-CNN + Gaussian process regression (GPR), when ensuring the localization accuracy in the indoor environment.