Abstract
Ultra-wideband (UWB) localization technique has been considered as a promising candidate for short-range positioning applications because of its advantages in terms of accuracy and penetrability. In this paper, backpropagation (BP) neural network is employed to improve the positioning accuracy of indoor environments. Theoretical analysis and simulation results show that the BP-based method outperforms other existing popular positioning algorithms with limited penalty in computational complexity. Therefore, it can be regarded as an alternative scheme for scenarios with the requirement of high position accuracy.
Z. Liang—This work was supported in part by the National Natural Science Foundation of China under Grant No. 61271262, in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2017JM6099, and in part by the Fundamental Research Funds for the Central University under Grant 310824171004.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, H., Liang, Z., Liu, D., Ma, L. (2018). Improved UWB Indoor Positioning Algorithms Based on BP Neural Network Model. In: Li, B., Shu, L., Zeng, D. (eds) Communications and Networking. ChinaCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 236. Springer, Cham. https://doi.org/10.1007/978-3-319-78130-3_13
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DOI: https://doi.org/10.1007/978-3-319-78130-3_13
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