# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
## 6 Conclusions  
1. In terms of the decreased positioning accuracy caused by the changes in NLOS errors due to UWB mobile node positioning, a UWB node positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was proposed in this paper. It classified the CIR signal characteristics by establishing fuzzy rules, adjusted the innovation value based on the change in the difference between the innovation and its variance in the KF algorithm, recognized and mitigated the NLOS errors and substituted the positioning estimation data into the LS positioning algorithm for node position estimation.
2. Static and dynamic experiments were conducted to verify the positioning algorithm based on fuzzy inference, the adaptive anti-NLOS KF positioning algorithm and the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm by the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. The experimental  
Figure 11: RMSE curves of the positioning trajectories.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Positioning Algorithm** & **X Direction RMSE** & **Y Direction RMSE** & **Overall Positioning** \\  & **(cm)** & **(cm)** & **RMSE (cm)** \\ \hline Adaptive Anti-NLOS KF & & & \\ Positioning Algorithm & 29.6038 & 29.8749 & 42.0582 \\ \hline Fuzzy Inference & & & \\ Algorithm & 20.0713 & 21.1351 & 29.1471 \\ \hline Positioning Algorithm & & & \\ Combining Fuzzy Inference with Adaptive Anti-NLOS KF & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mobile positioning of RMSEs in NLOS conditions.  
results demonstrated that the static positioning estimation and dynamic positioning trajectory of the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF were closer to the actual node position, the positioning performance was significantly improved, and the positioning accuracy was increased.
3. Because only three CIR signal characteristics (FPDL, RSSI and RT) were selected, the positioning accuracy of the positioning algorithm based on fuzzy inference might occasionally be significantly reduced. In the future, more CIR signal characteristics can be considered to improve the estimation accuracy of NLOS errors, thereby improving the positioning accuracy.  
Conceptualization, L.Z.; methodology, J.W. and Z.K.; software, Z.Z., J.W. and Z.K.; formal analysis, Z.Z. and S.Z.; data curation, S.Z.; writing--original draft preparation, J.W. and S.Z.; writing--review and editing, S.Z. and L.Z.; supervision, L.Z.; project administration, L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 61741303), the key laboratory of spatial information and geomatics (Guilin University of Technology) (No. 19-185-10-08), Scientific Research Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities in Guangxi (No. 2021 KY0260).  
The article does not cover human research, so it does not cover this item.  
Not applicable.  
The authors declare no conflict of interest.