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CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning

Yu, Xiuli ; Yang, Fenghao ; Yun, Jie ; Wu, Shu

Tehnički vjesnik, 2022-08, Vol.29 (4), p.1139-1147 [Periódico revisado por pares]

Slavonski Baod: University of Osijek

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Título:
CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning
Autor: Yu, Xiuli ; Yang, Fenghao ; Yun, Jie ; Wu, Shu
Assuntos: Artificial satellites in navigation ; deep-learning ; Impulse response ; Line of sight ; Machine learning ; Methods ; multi-flow neural networkindoor-localization ; Neural networks ; NLOS ; Signal classification ; Signal processing ; Ultra wideband technology ; Ultrawideband ; UWB
É parte de: Tehnički vjesnik, 2022-08, Vol.29 (4), p.1139-1147
Notas: 279445
Descrição: 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.
Editor: Slavonski Baod: University of Osijek
Idioma: Inglês;Eslovaco

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CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning

Yu, Xiuli ; Yang, Fenghao ; Yun, Jie ; Wu, Shu

Slavonski Baod: University of Osijek

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