# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 1 Introduction
In addition, its deployment on hardware platform is also a significant challenge. The existing NLOS mitigation methods suffer from an insufficient feature extraction of wireless signals, lack consideration of residual NLOS measurement errors during the position estimation process, and poor stability in the mixed positioning estimation results [32; 33; 34]. To address these issues, this study innovatively proposed an NLOS error identification, suppression, and position estimation method for 5G positioning terminals. The primary contributions of this study are outlined as follows:  
(1) A method for discriminating and classifying NLOS observations based on decoupling the features of wireless signals is proposed. This method initially divides the indoor propagation channels into multiple categories and utilizes the results of channel classification to assess the severity of the NLOS effects. Subsequently, it establishes selection criteria for filtering the ranging results of 5G positioning signals. (2) A position estimation method based on a square root unscented Kalman filter (SRUKF) is proposed. The square root of the state covariance is introduced, thereby \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Advantage** & **Shortcoming** & **Related Methods** \\ \hline \multirow{4}{*}{Detection} & The method is simple and easy to implement and can detect observations with obvious NLOS errors. & Redundant observations are required. And may cause an unnecessary loss of accuracy. & Consistency check [11; 12; 21], NLOS identification techniques [13; 14; 17; 22] \\ \hline \multirow{4}{*}{Mitigation} & Relatively low requirement of hardware equipment, and it can achieve high-precision positioning under NLOS error in a real environment. & Poor adaptability to noise and interference; the model is not general enough, and there is currently a lack of comprehensive theoretical analyses. & Machine learning techniques [16; 23; 24], support vector machines [15; 18; 25], vector tracking loop [26; 27] \\ \hline \multirow{4}{*}{Correction} & \multirow{4}{*}{Theoretically, the impact of NLOS errors can be completely eliminated.} & It requires a known high-precision map, which has high computational complexity and is difficult to deploy on commercial terminals. & Virtual station method based on ray tracing [28; 29], NLOS modeling [19; 30; 31] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods dealing with the NLOS error. avoiding the need for covariance reconstruction at each iteration. This approach offers advantages such as numerical stability and positive semi-definiteness of the state covariance. During the fusion process of mixed observations, it ensures the numerical stability of the filter's position estimation output. (3) We performed a comprehensive comparison of the existing advanced methods and built a 5G localization system to support the validation of the proposed method in real complex indoor environments. Both the simulation and experimental results demonstrate the superiority of the proposed NLOS mitigation and location estimation method over existing methods. The remainder of this paper is organized as follows. In Section 2, the 5G positioning reference signal measurement model is presented, and related problems are described. In Section 3, an NLOS mitigation and location estimation method for 5G positioning is proposed, which is composed of the derivation of positioning performance limits, measurement identification, and a location estimation method for NLOS mitigation. In Section 4, the performance of the proposed NLOS error mitigation algorithm is evaluated based on simulations as well as experimental tests. Finally, the discussion of this article is presented in Section 5.