## 3 NLOS Ranging Error and Correction Methods of UWB and Wi-Fi RTT Signals
### NLOS Ranging Error Mitigation  
Given the physical attributes of NLOS errors and the impact of substantial sample collections on real-time positioning performance, current research focuses on NLOS error mitigation through real-time NLOS/Line-of-Sight (LOS) identification. The Wi-Fi RTT protocol, known for generating systematically biased ranging measurements, requirescalibration for precise ranging and positioning outcomes. Studies indicate that Wi-Fi RTT exhibits larger ranging errors in NLOS scenarios compared to LOS conditions [48, 49]. Various methods have been reported to enhance ranging accuracy using RTT measurements [50] or multiple RTT measurements from diverse frequency bands [51]. These identification and correction methods primarily include:  
Threshold-based identification: The threshold method compares specific feature parameters with a predefined threshold to identify NLOS signals and make corresponding corrections. In [52], a threshold was implemented to identify the positioning error caused by NLOS. First, the initial tag position coordinates were obtained by using the Chan algorithm using the quadratic weighted least squares method. Then, a suitable threshold was set to compare with the difference in distance. The results greater than the threshold were iteratively optimized by the particle swarm algorithm and then output. The results lower than the threshold were directly output. Although these methods are easy to implement, their disadvantages are that the threshold selection depends on the environment and scenario and is sensitive to noise. Statistical models: A variety of statistical models, which are based on the probability of NLOS propagation, have been reported for identifying NLOS instances and improving the ranging measurements. In [53], a constrained weighted least squares algorithm was developed for TDOA positioning in the presence of NLOS signals. A quadratic programming algorithm was reported for improving TOA positioning [54]. Another approach first constructs a scene recognition model based on Gaussian process regression to identify NLOS and LOS measurements, subsequently establishing an error calibration model for ranging correction. Owing to the frequency diversity, measurements from various frequency channels are considered unrelated and they are weighted and averaged to effectively mitigate NLOS error [51]. Notably, a kernel function was employed to reduce noise, significantly enhancing the robustness [55]. A ranging error model based on the Gaussian mixture model effectively addresses non-Gaussian ranging errors caused by multipath effects [56]. Bayesian filtering: As ranging measurements are time series, the Bayesian filtering methodology has been utilized for enhancing the accuracy and robustness of ranging measurements in the presence of NLOS conditions. The Extended Kalman Filter (EKF) was utilized to minimize the effect of NLOS errors in TDOA positioning solutions [57]. The authors of [58] introduced the optimization concept and fused TDOA data using a simulated annealing algorithm, while [59] reported a joint TOF and TDOA algorithm to improve the positioning accuracy. Several studies explored the data fusion using the Bayesian filtering approach, for example, fusing either UWB or Wi-Fi RTT with IMU data. In [60], the EKF was utilized for data fusion and CIR data were leveraged to identify NLOS measurements. The Bayesian filtering approach has been applied to locate mobile devices using a set of ranging measurements, and measurements of inertial sensors are integrated for improving the positioning accuracy [61, 62, 63, 64, 65]. Machine learning: Machine learning models have been proven to be an efficient approach for identifying NLOS signal patterns in received signals [66]. The authors of [9] developed a machine learning method that can identify NLOS signals of Wi-Fi using data from one second. This study highlighted that the proposed machine learning method segmented the samples for NLOS/LOS conditions, achieving over 96% discrimination accuracy with a sample size of 10.