NLOS Measurement Identification Based on TDOA in Mixed NLOS-LOS Environments

Under the premise that all non-line-of-sight (NLOS) base stations are identified and the remaining number of line-of-sight (LOS) base stations is sufficient, the localization accuracy of time-difference-of-arrival (TDOA) algorithms can achieve higher localization accuracy. However, existing NLOS identification algorithms based on TDOA measurements perform poorly when there is more than one NLOS base station. This study proposes an NLOS measurement identification algorithm for mixed NLOS-LOS environments, which is relatively insensitive to the number of NLOS base stations. First, the base stations are grouped, and the cost function of the Maximum Likelihood (ML) estimation is used to select the base stations likely to be LOS. Then, hypothesis testing determines if the remaining base stations include any NLOS stations. Finally, the remaining LOS base stations are used to calculate the target position. Simulation and experimental results demonstrate that the proposed method has a high NLOS measurement identification rate and localization accuracy.