# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering  
[PERSON]  
1 College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 51004, China; [EMAIL_ADDRESS] (J.W. ); zsslgglut.edu.cn (S.Z. ); [EMAIL_ADDRESS] (Z.K. )1  
[PERSON]  
2 Network and Information Center, Guilin University of Technology, Guilin 51004, China; [EMAIL_ADDRESS] 2  
[PERSON]  
1 College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 51004, China; [EMAIL_ADDRESS] (J.W. ); zsslgglut.edu.cn (S.Z. ); [EMAIL_ADDRESS] (Z.K. )1  
[PERSON]  
1 College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 51004, China; [EMAIL_ADDRESS] (J.W. ); zsslgglut.edu.cn (S.Z. ); [EMAIL_ADDRESS] (Z.K. )1  
[PERSON]  
1 College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 51004, China; [EMAIL_ADDRESS] (J.W. ); zsslgglut.edu.cn (S.Z. ); [EMAIL_ADDRESS] (Z.K. )1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/14/09/](https://www.mdpi.com/journals/pdf/14/09/)]([https://www.mdpi.com/journals/pdf/14/09/](https://www.mdpi.com/journals/pdf/14/09/))  
mitigation of NLOS errors have become hot topics in the research on UWB positioning [7; 8]. The existing methods for handling the NLOS errors mainly include [9; 10]: (1) realizing integrated UWB positioning by adding hardware other than UWB, (2) identifying and mitigating the NLOS errors by virtue of filtering algorithms, (3) mitigating the NLOS errors with the positioning algorithm based on convex optimization and (4) identifying and mitigating the NLOS errors based on the channel impulse response (CIR) signal characteristics. [PERSON] et al. [11] proposed a UWB/INS integrated location method based on an improved robust Kalman filter and support vector regression (SVR). The robust Kalman filter (RKF) is improved, and the enhanced IGG3 weight function is used to modify the innovation piecewise to reduce the influence of abnormal measurement information on the filtering result. [PERSON] et al. [12] put forward a UWB-GPS combined positioning scheme, in which filtering is achieved with plane coordinates, speed and azimuth angle as observation vectors, thus improving the positioning accuracy. [PERSON] [13] proposed a Wi-Fi/UWB combined positioning algorithm. The algorithm first uses the ultra-wideband ranging value to filter the Wi-Fi fingerprint points. After the matching and positioning, the positioning result is added to the neighbor set as a new neighbor. The algorithm finds the nearest neighbor to the UWB base station among these neighbors and uses the position coordinates corresponding to this neighbor as the final positioning result. Moreover, by combining the inertial measurement unit and UWB, [PERSON] [14] proposed an indoor positioning method that applies UWB ranging and inertial navigation fusion. A tightly coupled Kalman filter was adopted, where UWB ranging values were taken as the extended Kalman filter observation quantity, the position and attitude of the inertial navigation were taken as the extended Kalman filter prediction value, and UWB ranging values were used to constantly correct the position and attitude data of the inertial navigation solution. [PERSON] et al. [15] applied the KF algorithm to study the change rules in residual errors in both LOS and NLOS indoor environments. Moreover, an appropriate threshold was set and compared with the real-time residual error of ranging to identify and mitigate the NLOS errors. [PERSON] et al. [16] established the robust factor identification using the KF innovation vector and the threshold in the LOS environment, which is utilized to identify the NLOS errors and attenuate the influences of NLOS ranging errors and abnormal ranging values.