# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 1 Introduction  
Temporal and spatial information is essential to human existence and development. Location-based services (LBS) utilize positioning and navigation technologies to provide users with a series of services based on temporal and spatial locations, playing an increasingly important role in various emerging fields such as smart cities, intelligent transportation, and industrial internet. Ubiquitous and trustworthy location services require the support of advanced positioning technologies. In outdoor environments, GNSSs can provide high-precision location information. However, due to the low penetrability of satellite signals, it is not possible to offer users high-precision location services in non-exposed spaces such as indoor and underground environments [1; 2]. Currently, positioning technologies in satellite-denied environments worldwide include Bluetooth, UWB, WLAN, and mobile communication networks [3]. Among these, mobile communication networks are the most widely used wireless networks for ground coverage. In particular, 5G networks, which can provide high-precision location services based on mobile communication networks, have become a research hotspot [4; 5; 6; 7; 8]. The primary technical bottleneck in improving 5G indoor positioning accuracy is the NLOS effect [9]. In complex environments such as indoors and underground, wireless signal propagation is severely obstructed and reflected, resulting in NLOS phenomena [10]. Since communication services in mobile communication networks are insensitive to delays, NLOS phenomena have a minor impact on communication quality. However, for positioning, NLOS phenomena can lead to increased positioning observation errors. The errors caused by NLOS phenomena may be much largerthan the errors introduced by the ranging capability itself. Therefore, researching NLOS error suppression techniques in mobile communication networks is of great significance for achieving high-precision positioning. The process of NLOS effect suppression mainly consists of two parts. Firstly, the sources in NLOS states are identified, and then the influence of NLOS effects on the positioning results is suppressed through filtering or compensation methods. A common method for NLOS identification is to use residual magnitudes to judge the accuracy of signal measurements after positioning. Observations with large residuals are identified as being in NLOS states. [PERSON] et al. found that observations with larger residuals mostly come from signals in NLOS channel states, and they judged whether NLOS observations are used in the current positioning based on the mean residual [11]. [PERSON] et al. studied Time-of-Flight (TOF) positioning through first-group observations from different base stations and then performed initial positioning separately. They calculated the residual between the positioning result and the TOF ranging result to identify TOF measurements with large residuals as being in NLOS states [12]. [PERSON] et al. used a similar method to judge the LOS and NLOS states of Time-Difference-of-Arrival (TDOA) observations [13]. These methods can effectively identify observations in NLOS states, but they require a high redundancy of observation information and cannot be used when there are few observation data points. Wireless signal statistical characteristics can be utilized to identify channel states and thus recognize NLOS effects. For instance, [PERSON] et al. utilized the kurtosis of signal amplitudes to differentiate between Line-of-Sight (LOS) and NLOS channel states. Results from experiments involving various systems indicate that signals in NLOS states exhibit significantly smaller kurtosis values compared to those in LOS states [14]. [PERSON] and [PERSON] analyzed the differences in the standard deviation of the angle of arrival (AOA) between LOS and NLOS states. They proposed NLOS identification methods based on the AOA and a joint method based on the Time of Arrival (TOA) and signal strength, which are applicable under different equipment conditions [15].