Abstract
Indoor localization and tracking become more and more popular in our daily life. Due to the time consuming of Wi-Fi based location and error accumulation of motion sensor based location. A timely, enduring location system with high accuracy is difficult to be made. In this paper we present an effective combined model to handle this problem: we use ELM regression algorithm to predict position based on emotion sensor, and then combine the Wi-Fi location result to motion sensor based location result using particle filter. The experiments show that we can get higher accurate location result in every 200ms. And the trajectory is smoother as the real one than traditional Wi-Fi fingerprinting method.
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References
Bahl, P., et al.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of the IEEE Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)
Carroll, et al. An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference (2010)
Huang, G.-B., et al.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE (2004)
Liu, J., Chen, Y., et al.: SELM: Semi-supervised ELM with application in sparse calibrated location estimation. Neurocomputing 74(16), 2566–2572 (2011)
Liu, J., Guyang, et al.: Incremental Localization in WLAN Environment with Timeless Management. Chinese Journal of Computers 36(7), 1448–1455 (2013)
Chen, Z., et al.: Online Sequential ELM based Transfer Learning for Transportation Mode Recognition. In: The 6th IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2013), pp. 78–83 (2013)
Gustafsson, F., et al.: Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Processing 50(2), 425–437 (2002)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Thrun, S.: Simultaneous localization and mapping. In: Jefferies, M.E., Yeap, W.-K. (eds.) Robot. & Cogn. Approach. to Spat. Map. STAR, vol. 38, pp. 13–41. Springer, Heidelberg (2008)
Tuchler, M., Singer, A.C., Koetter, R.: Minimum mean squared error equalization using a priori information. IEEE Transactions on Signal Processing 50(3), 673–683 (2002)
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Jiang, X., Chen, Y., Liu, J., Gu, Y., Chen, Z. (2015). Wi-Fi and Motion Sensors Based Indoor Localization Combining ELM and Particle Filter. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_11
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DOI: https://doi.org/10.1007/978-3-319-14066-7_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
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