Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning

摘要

Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence of latent codes are injected into an LSTM network to identify location. To assess the accuracy and effectiveness of our model, we perform extensive real-life experiments.