EFK Localization Method in NLOS Environment Based on Residual Discrimination

To solve the problem of large positioning errors in complex Non-Line-of-Sight (NLOS) environments, an improved extended Kalman filter (EKF) method based on the time of arrival (TOA) and angle of arrival (AOA) is proposed in this paper. The method utilizes residuals to discriminate the severity of the current location point affected by the NLOS scene, the Kalman gains of different regions affected by the NLOS scene are treated differently by the method through confidence interval division, and the TOA and AOA two-dimensional observation values are used for extended Kalman filtering for tracking and localization. The simulation results demonstrate that in NLOS environment, the proposed strategy improves localization accuracy by approximately 19.72% when compared to the two-dimensional Kalman filtering method based on TOA and AOA, and the localization accuracy of the proposed method is improved by approximately 35.96% and 68.72% compared with the unscented Kalman filter (UKF) and the traditional Kalman filter (KF) methods, respectively.