Abstract
To overcome the problem of dimension curse in the processing of predicting indoor location by using the traditional Markov chains, this paper proposes a novel hybrid Markov-LSTM model to predict the indoor user’s next location, which adopt the multi-order Markov chains (k-MCs) to model the long indoor location sequences and use LSTM to reduce dimension through combining multiple first-order MCs. Finally, we conduct comprehensive experiments on the real indoor trajectories to evaluate our proposed model. The results show that the Markov-LSTM model significantly outperforms five existing baseline methods in terms of its predictive performance.
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Funding
This project was supported by National Key Research and Development Program of China, (Grant Nos. 2016YFB0502104, 2017YFB0503500), and Digital Fujian Program (Grant No. 2016-23).
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Wang, P., Wu, S., Zhang, H. (2020). Predicting Indoor Location based on a Hybrid Markov-LSTM Model. In: Di Martino, S., Fang, Z., Li, KJ. (eds) Web and Wireless Geographical Information Systems. W2GIS 2020. Lecture Notes in Computer Science(), vol 12473. Springer, Cham. https://doi.org/10.1007/978-3-030-60952-8_4
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DOI: https://doi.org/10.1007/978-3-030-60952-8_4
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