A Hybrid Markov and LSTM Model for Indoor Location Prediction

Accurate and robust indoor location prediction plays an important role in indoor location services. Markov chains (MCs) have been widely adopted for location prediction due to their strong interpretability. However, multi-order Markov chains (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-MCs) are not suitable for predicting long sequences due to problems of dimensionality. This study proposes a hybrid Markov model for location prediction that integrates a long short-term memory model (LSTM); this hybrid model is referred to as the Markov-LSTM. First, a multi-step Markov transition matrix is defined to decompose the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-MC into multiple first-order MCs. The LSTM is then introduced to combine multiple first-order MCs to improve prediction performance. Extensive experiments are conducted using real indoor Wi-Fi positioning datasets collected in a shopping mall. The results show that the Markov-LSTM model significantly outperforms five existing baseline methods in terms of its predictive performance.