## 6 Conclusions  
In GNSS-denied urban or indoor environments, accurate localization is still an unsolved problem due to the unavailability or degradation of GNSS signals. For GNSS-denied urban or indoor environments. TOA-based method is the most common localization method due to high accuracy and simplicity. The localization accuracy could be dramatically degraded if the NLOS signals are misapplied as LOS signals in TOA-based methods. Identifying and localization with the LOS BS can provide performance close to CRLB when the identification algorithm has high identification accuracy and there are at least three LOS BSs. In this paper, a two-step LOS signal identification algorithm exploiting the geometric relationship has been proposed. In the first step, all BSs are divided into multiple BS combinations with every three BSs, the TOA distance error of each BS combination is estimated based on common chord intersection point position deviation from MS, the BS combinations whose TOA distance error estimate satisfy Gaussian distribution are roughly identified as LOS BS combination and enter the second step, the other BS combinations are discarded as NLOS BS combination. In the second step, mutual distance threshold is introduced to judge whether two common chord intersection points are close to each other and whether they are near MS at the same time. On this basis, discrimination result matrix for all common chord intersection points is obtained and then LOS BS combinations are identified. The BSs of LOS BS combinations are identified as LOS BS, and the signals received at LOS BS are identified as LOS signal ultimately. Simulation results show that the proposed algorithm outperforms the other two geometric constraint methods. Simulation results also confirm the effectiveness of our theoretical derivation, which provides a theoretical basis for the setting of the identification threshold value and facilitates the application of proposed algorithm.  
Conceptualization and methodology, R.L. and H.Z.; analysis, R.L. and H.Z.; writing--original draft preparation, R.L. and H.Z.; writing--review and editing, Y.K. and P.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Nature Science Foundation of China [No. 61871332], Sichuan Science and Technology Program [grant numbers: 2021 YFG0169], [grant numbers: 2022 YFG0190] and Chengdu Normal University Science and Technology Project [no. 111/11159001].  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**The Number of LOS BS**} & \multicolumn{3}{c}{**The Number of Correctly Identified**} \\ \cline{2-4}  & **RT** & **AM** & **Proposed** \\  & **Algorithm** & **Algorithm** & **Algorithm** \\ \hline
3 & 729 & 823 & 823 \\ \hline
4 & 943 & 941 & 950 \\ \hline
5 & 955 & 964 & 976 \\ \hline
6 & 943 & 975 & 978 \\ \hline
7 & 936 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm performance comparation (\(\sigma=18\)).  
**Acknowledgments:** We would like to thank the reviewers for their valuable comments and pointing out possible further research topics.