An Attention-Assisted UWB Ranging Error Compensation Algorithm

Abstract

Ultra wide-band (UWB) brings many benefits to wireless sensing. Theoretically, UWB can achieve centimeter-level ranging accuracy. However, in practical applications, multipath fading channel (MPF) problems and antenna delay effects will adversely affect the ranging accuracy of UWB. The sample data of UWB contain rich channel characteristics, which can be extracted to characterize the implicit relation of UWB ranging error. Undoubtedly, the UWB channel characteristics will show different importance in characterizing UWB ranging errors in different environments. Therefore, this letter proposes an attention-assisted UWB ranging error compensation algorithm. Using the attention mechanism, the significance of the extracted UWB channel characteristics in various environments can be re-evaluated to improve the performance of the deep neural network (DNN) model. The experimental results prove that using the proposed algorithm, the 75% error lines of the original indoor and outdoor ranging errors are reduced from 13.32 cm and 19.41 cm to 5.74 cm and 5.05 cm, and median errors are compensated from 7.00 cm and 15.42 cm to 2.78 cm and 2.69 cm, respectively.