CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning
Xiuli Yu, Fenghao Yang, Yun, Jie, Shu Ya Wu · Tehnicki vjesnik - Technical Gazette · 2022
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-ofsight (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.