Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints
This work addresses indoor positioning for smartphone users, presenting an incremental improvement by integrating existing deep learning components.
The paper tackles the high-dimensional time-series problem of indoor positioning using WiFi fingerprints by proposing the Convolutional Mixture Density Recurrent Neural Network (CMDRNN), which combines CNNs, RNNs, and MDNs to predict user location, with results demonstrating effectiveness on a real-world dataset.
Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series prediction problem can be very tricky to solve. To address this issue, we propose a novel deep learning model, the convolutional mixture density recurrent neural network (CMDRNN), which combines the strengths of convolutional neural networks, recurrent neural networks and mixture density networks. In our model, the CNN sub-model is employed to detect the feature of the high dimensional input, the RNN sub-model is utilized to capture the time dependency and the MDN sub-model is for predicting the final output. For validation, we conduct the experiments on the real-world dataset and the obtained results illustrate the effectiveness of our method.