UWB indoor localization method based on neural network multi-classification for NLOS distance correction

It is well known that ultra-wideband (UWB) is widely used in building indoor positioning systems (IPS) because of its unique advantages. However, compared with the line-of-sight environment (LOS), UWB localization on none-line-of-sight (NLOS) channels has certain limitations, which will reduce the UWB ranging accuracy and location reliability in indoor environment. In this paper, a neural network (NN)-enhanced UWB positioning method is proposed. It can improve positioning performance by using the received channel impulse response (CIR) and UWB raw ranging data to classify the channel conditions and predict the distance. By training CNN-LSTM and MLP neural networks, the proposed method can alleviate the deterioration of localization performance caused by NLOS. The experimental results showed that the average NLOS recognition accuracy of five different obstacles including wooden doors, concrete walls, metal shelves, human body and glass windows reaches up to 92.36 %. In addition, the average root mean square error (RMSE) between the predicted distance and the true distance was 0.3123 m. The indoor positioning test was carried out by weighted least squares (WLS) and the average positioning error under three trajectories was 0.1223 m, which improved the performance by 83.56 % compared with the original UWB positioning system, thus proving its ability to reduce positioning degradation.