# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
Meanwhile, the positioning accuracy is improved by real-time estimation of system noise using the Sage-Husa filtering. Based on LOS/NLOS scene recognition, [PERSON] et al. [17] proposed a TDOA location algorithm based on particle filter and maximum likelihood. Firstly, the approximate maximum likelihood TDOA algorithm was used to obtain a preliminary location result without distinguishing between LOS and NLOS scenes; then, the preliminary results were corrected with particle filter to reduce the increase in positioning error and inconsistency of positioning results caused by LOS and NLOS scene switching. In addition, [PERSON] et al. [18] introduced the \"balance parameter\" related to NLOS errors and proposed a new robust weighted LS problem with the target position and the NLOS balance parameter as the estimation variables. For the identification of the UWB channel state, [PERSON] et al. [19] designed a statistical model by incorporating the RSSI of CIR signals and root mean square (RMS) delay characteristics. Furthermore, [PERSON] et al. [20] developed a machine-learning model for an LS support vector machine based on the CIR channel characteristics, which can not only judge NLOS errors but also reduce the ranging errors resulting from NLOS errors. However, the algorithm accomplishes the identification and mitigation of NLOS errors in two steps, thus increasing its overall complexity. To increase the ranging accuracy, [PERSON] [21] put forward a machine-learning method for NLOS error mitigation with low complexity based on a sparse pseudo-input Gaussian process. Among the above methods, adding hardware for integrated positioning can improve the positioning accuracy, but such method undoubtedly increases the total cost of the positioning system and the cost of the data fusion algorithm. Given the possible influence of NLOS errors on the measured range information, the methods based on ranging estimation will have a less favorable effect on identifying and mitigating NLOS errors. Though the machine-learning-based algorithms have the best effect on NLOS error identification and mitigation, they are not suitable for occasions with strong timeliness due to their high complexity. As the fuzzy inference algorithms possess the characteristics where no statistical model construction and data training are needed and given their high operating speed, they can mitigate the NLOS and LOS errors and improve the positioning accuracy in the case of estimating ranging errors of CIR signal characteristics, conforming to the needs of UWB node positioning [22]. The anti-NLOS KF algorithm can conduct smoothing filtering on the data and realize identification and mitigation of NLOS errors, thus effectively weakening the influence of ranging errors on positioning during UWB mobile node positioning and improving the positioning accuracy [23; 24]. Based on the advantages of these two algorithms, a UWB positioning algorithm combining the fuzzy inference for NLOS error mitigation with adaptive anti-NLOS KF was proposed in this paper. In this algorithm, a fuzzy inference system of the ranging errors was first established using the changes in RSSI, first path power level (FPPL) and rise time (RT) characteristics of the CIR signals collected via ranging, and the ranging errors were initially mitigated by the fuzzy inference method. Then, the adaptive anti-NLOS KF algorithm was employed to process the range data after fuzzy inference, and the triple innovation variance was compared with the innovation to identify the NLOS errors. Moreover, the value of the innovation correction factor was automatically adjusted according to the changes in differences between innovation and innovation variance, so as to conduct second mitigation for the NLOS errors after fuzzy inference. Ultimately, the final estimated range information was substituted into the LS positioning algorithm for node positioning.