# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 1 Introduction
[PERSON] et al. employed machine learning techniques to combine various signal features such as maximum received power, power kurtosis, power skewness, root mean square delay spread, Ricean K-factor, and angle-of-arrival spread. This approach enhances the dimensionality of information and improves NLOS identification effectiveness [16]. The current mainstream NLOS suppression methods can be categorized into error compensation-based suppression methods and filtering-based suppression methods. Error compensation-based methods involve compensating for measurements from NLOS states and using corrected measurements for positioning. Following a similar approach, [PERSON] et al. utilized statistical differences in signal delays between LOS and NLOS channel states to identify channel states. They corrected temporary NLOS observations using measurements from LOS states, thereby effectively improving positioning accuracy [17]. Based on filtering-based suppression methods, once the NLOS states of signal sources are identified, they are treated differently from LOS sources. This involves either discarding observations from NLOS states directly or suppressing their influence on positioning results through weighting. [PERSON] et al. utilized the statistical differences in received signal strength characteristics between LOS and NLOS wireless channel states to filter observations. They discarded measurements from NLOS states and selected TOF measurements from LOS states for positioning [18]. [PERSON] and [PERSON], after identifying NLOS states, assigned smaller weights to NLOS TOA and DOA measurements using weighted least squares to mitigate the impact of NLOS on the positioning results [19]. [PERSON] et al. considered the impact of filtering results on the geometric configuration while conducting NLOS source filtering. They selected NLOS sources that contributed significantly to the geometric accuracy and assigned smaller weights to them. This approach suppresses NLOS effects while preventing a significant degradation in geometric accuracy [20]. The existing NLOS suppression technology is mainly divided into three categories, as shown in Table 1.  
The first category is detection, which involves initially identifying the observations of positioning signals. Observations that may be affected by NLOS are either directly removed or minimized in terms of their weight. The advantage of this approach is its simplicity, while its disadvantage is the lack of classification. Ref. [21] proposed the consistency checking method to examine the residual range measurement or navigation solution, which is similar to receiver autonomous integrity monitoring. The second category is mitigation, which uses multiple features to classify the received positioning signals, and involves performing error suppression based on the observation feature classification results. Technologies such as machine learning and support vector machines have been widely used to classify and perform the feature extraction of GNSSs and wireless communication network observations [23; 24; 25; 26]. [PERSON]. [27] proposed the reception of NLOS signals based on vector tracking loop and initially proved the effectiveness of the proposed method through field tests. However, there is currently a lack of comprehensive theoretical derivation and performance limit analyses for this type of method. The third type is correction. In this type of technique, NLOS paths are considered useful signals. In utilizing known three-dimensional maps for assisted positioning, potential reflection paths are inferred, and error correction is performed accordingly [28; 29; 30; 31]. This is equivalent to obtaining a new observation quantity that is not affected by non-line-of-sight errors. This type of method can ideally remove the impact of NLOS errors on positioning and can even bring additional improvements in performance. However, the computational complexity is very high, and it is difficult to meet real-time requirements.