UWB Non-line-of-Sight Propagation Identification and Localization

This chapter focuses on non-line-of-sight (NLOS) identification and mitigation for UWB localization. First, an NLOS identification method based on One-Dimensional Wavelet Packet Analysis (ODWPA) and Convolutional Neural Network (CNN) is proposed, which achieves an average identification accuracy of about 95%, significantly higher than that of other traditional methods. Then, two different error models in LOS/NLOS environments are established respectively, which improve the ranging accuracy by about 29% on average. Finally, an improved Chan-Kalman localization algorithm based on NLOS identification is proposed. The experimental results show that, compared with other algorithms, the proposed algorithm achieves the highest localization accuracy in static scenario with an average of 12.6 cm, which is an improvement of about 32.8%. In dynamic scenario, the average error of the proposed algorithm is about 10.7 and 8.9 cm in X-axis and Y-axis, respectively, significantly outperforming other algorithms.