Technical gazette, Vol. 29 No. 4, 2022.
Original scientific paper
https://doi.org/10.17559/TV-20220315024522
CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning
Xiuli Yu
; Beijing University of Posts and Telecommunications, Xitucheng Road, Haidian District, Beijing, China
Fenghao Yang
orcid.org/0000-0001-8186-6364
; Beijing University of Posts and Telecommunications, Xitucheng Road, Haidian District, Beijing, China
Jie Yun
; Beijing University of Posts and Telecommunications, Xitucheng Road, Haidian District, Beijing, China
Shu Wu
; Beijing University of Posts and Telecommunications, Xitucheng Road, Haidian District, Beijing, China
Abstract
Non-line-of-sight (NLOS) error is the main factor that reduces indoor positioning accuracy. Identifying NLOS signals and eliminating NLOS errors are the keys to improving indoor positioning accuracy. To better identify NLOS signals, a multi-stream model channel-impulse-response-neural-network (CIRNN) was proposed. The inputs of CIRNN include the channel impulse response (CIR) and a small number of channel parameters. To make a more obvious comparison between NLOS signals and line-of-sight (LOS) signals, a new energy normalization method is proposed. Fusing multi-dimensional features, the CIRNN network has a good convergence performance and shows stronger sensitivity to NLOS signals. Experimental results show that the CIRNN achieves the best accuracy on the open-source data set, the F1 score is 89.3%. At the same time, the working efficiency of CIRNN meets industry needs, CIRNN can refresh the target position at about 92.6 Hz per second.
Keywords
deep-learning; multi-flow neural networkindoor-localization; NLOS; signal processing; UWB
Hrčak ID:
279445
URI
Publication date:
17.6.2022.
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