Author:
Tian Yalin,Lian Zengzeng,Wang Penghui,Wang Mengqi,Yue Zhe,Chai Huabin
Abstract
AbstractUltra-wideband technology has good anti-interference capabilities and development prospects in indoor positioning. Since ultra-wideband will be affected by random errors in indoor positioning, to exploit the advantages of the Kalman filter (KF) and the long short-term memory (LSTM) network, this paper proposes a long short-term memory neural network algorithm fused with the Kalman filter (KF–LSTM) to improve UWB positioning. First, the ultra-wideband data is processed through KF to weaken the noise in the data, and then the data is fed into the LSTM network for training, and the capability of the LSTM network to process time series features is employed to obtain more accurate label positions. Finally, simulation and measurement results show that the KF–LSTM algorithm achieves 71.31%, 37.28%, and 49.31% higher average positioning accuracy than the back propagation (BP) network, (back propagation network fused with the Kalman filter (KF-BP), and LSTM network algorithms, respectively, and the KF–LSTM algorithm performs more stably. Meanwhile, the more noise the data contains, the more obvious the stability contrast between the four algorithms.
Funder
the Universities of Henan Province
the Doctoral Scientific Fund Project of Henan Polytechnic University
Henan Polytechnic University Funding Plan for Young Backbone Teachers
the Natural Science Foundation of Henan Province
the National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference38 articles.
1. Xu, R., Chen, W., Xu, Y. & Ji, S. A new indoor positioning system architecture using GPS signals. Sensors 15, 10074–10087. https://doi.org/10.3390/s150510074 (2015).
2. Xu, J.-C., Lian, Z.-Z., Dong, J.-Q. & Yue, Z. Anti-multipath error of BDS based on WPT decomposition and reconstruction algorithm. Sci. Technol. Eng. 22, 15477–15484 (2022).
3. Poulose, A., Kim, J. & Han, D. S. A sensor fusion framework for indoor localization using smartphone sensors and Wi-Fi RSSI measurements. Appl. Sci. 9, 4379. https://doi.org/10.3390/app9204379 (2019).
4. Zhuang, Y., Yang, J., Li, Y., Qi, L. & El-Sheimy, N. Smartphone-based indoor localization with bluetooth low energy beacons. Sensors 16, 596. https://doi.org/10.3390/s16050596 (2016).
5. Minne, K. et al. Experimental evaluation of UWB indoor positioning for indoor track cycling. Sensors 19, 2041. https://doi.org/10.3390/s19092041 (2019).
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献