###### Abstract
This way usually requires assumption or prior knowledge about the scatterer, BS or MS, which limits its applicability in many situations. The third way identifies LOS and NLOS signals first, and then discards NLOS signals, so that only the LOS signals are applied for localization [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]; unlike the other two ways, it requires at least three LOS signals for localization. The performance of the third way will be close to the Cramer-Rao lower bound (CRLB) when the algorithm has high identification accuracy, therefore, accurately identifying the LOS signals and using them for localization can improve the localization accuracy greatly, we will consider the third way in this paper. For the identification of LOS/NLOS signals, the algorithms can be mainly divided into three categories. The first category is machine learning method [28; 29; 30; 31], based on training data set and machine learning algorithm, LOS/NLOS signals can be identified. This category of methods requires a large amount of training data for pre-training, and the independent identical distribution (i.i.d) of data cannot be guaranteed usually due to different practical scenarios. The second category is the statistical signal method, which utilizes statistical characteristics of received signal to achieve the identification of LOS/NLOS signals [32; 33; 34; 35]. This category of methods requires priori knowledge of signals, which is an unrealistic assumption in practical scenario usually. The third category is the geometric constraint method, which identifies LOS/NLOS signals based on the spatial geometric relationship between the sensors and source. For the reason of simplicity, only requiring TOA measurements, and without requiring priori knowledge and a large amount of training datasets, the geometric constraint method has received immense attention in the research society [36; 37; 38; 39]. In geometric constraint methods, reference [36] provided residual testing (RT) algorithm, which adopted approximate maximum likelihood (AML) to estimate the position of signal source in recursive procedure based on TOA measurements, if all measurements are LOS signals, then the residuals normalized by the Cramer-Rao lower bound, will have a central Chi-square distribution. If there are NLOS signals, the distribution is noncentral Chi-square. Consequently, LOS/NLOS signals were identified by the residual testing. The authors of [37; 38] improved the algorithm of [36] in computational complexity. The authors of [39] provided a two steps area measurement (AM) algorithm. In the initial identification step, the BS combinations including three BSs, whose overlapping area are smaller than threshold or not being, are identified as LOS BS combinations. Then, residual test, overlapping area measurement or statistical method are adopted to identify the LOS signal ultimately. Compared with the RT algorithm, the AM algorithm improves identification accuracy greatly, but the setting of the identification threshold value lacks a theoretical basis, which limits its applicability in practical environments. In this paper, a novel geometric constraint LOS signal identification method is proposed to improve identification accuracy of LOS signals. Compared with the above two geometric constraint algorithms, the proposed method has better identification accuracy and is more convenient to implement in a practical scenario. The main contributions of this paper are three folds. 1. Better identification accuracy. We propose a two-step LOS signal identification algorithm based on common chord intersection point position deviation from MS. Compared with RT algorithm and AM algorithm, the proposed algorithm has better identification accuracy. 2. Providing the basis of the setting of the identification threshold value. Based on the characteristics of LOS error and Gaussian distribution, we derive the setting of identification threshold value, which facilitates the application of the proposed algorithm.