Research on Ultra-wideband NLOS/LOS Classification Algorithm Based on CNN-LSTM
In ultra-wideband(UWB)indoor positioning systems,the identification and classification of non-line-of-sight(NLOS)signals is a key technology that affects positioning accuracy.In order to solve the problem of noise interference in the chan-nel impulse response(CIR)used for NLOS identification and the difficulty of threshold selection in multiple scenarios of traditional NLOS identification methods,this paper adopts a reversible transform for CIR data denoising method,and uses convolutional neural network(CNN)combined with long short-term memory network(LSTM)structure to identify NLOS signals.The open source data set is used for training and verification.The results show that the classification accuracy of CNN-LSTM after CIR denoising can reach up to 90.62%,indicating that this method can effectively improve the classification accuracy of NLOS signals.
万方数据
维普
