An Indoor Positioning Method Based on Convolutional Neural Network
According to the problem of low positioning accuracy caused by unstable wireless access point sig-nals in actual indoor positioning scenarios,an indoor positioning method based on convolutional neural network is proposed.The algorithm includes an offline stage and an online stage.The offline stage mainly completes the ac-quisition of wireless access point signals,which is used as the training data for the convolutional neural network model.The online stage uses the trained model to estimate the area where the location is located,and finally the weighted k-nearest neighbour algorithm is used to calculate the exact position coordinates.By comparing with SVR and KNN algorithms,the results show that it is superior to other algorithms in the two-dimensional planar regression positioning.
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