An Encoded LSTM Network Model for WiFi-based Indoor Positioning

WiFi received signal strength (RSS)-based fingerprinting has been widely adopted in many indoor positioning systems due to its implementation simplicity and low computational complexity. Recently, some studies have explored the potential temporal features among WiFi data to provide better positioning accuracy using Long Short-Term Memory (LSTM). However, the large volume of invalid RSS signals/values in the radio map degrades the training performance and the positioning accuracy. Some recent research has shown effort to reduce the impact of the problem mentioned above using deep learning. However, these either lack efficiency (repeated training needed or training resource wasted) or cannot provide precision position estimations. This paper proposes a novel Encoded LSTM network model to efficiently extract main features from the WiFi RSS data and provide high positioning accuracy. Experimental results based on an open-source crowdsourced dataset show that the encoded LSTM can reduce the dimension of the WiFi fingerprints from 992 to 64 and achieve a mean positioning error of 7.37m. Compared to the benchmark results based on the same dataset, the encoded LSTM shows the lowest mean error, which outperforms 13 popular positioning algorithms. The proposed encoded LSTM can also provide a 10% improvement in positioning accuracy in comparison to other conventional LSTM models.