###### Abstract
However, the residual-based method requires a sufficient number of LOS BSs for localization, which cannot be guaranteed in practical localization scenarios and NLOS propagation model compensation requires explicit knowledge of the NLOS propagation model or historical measurements, which has limitations in practical applications. The third category is the virtual station method, which locates the scatterers in the NLOS environment first, and then uses scatterers as virtual stations, so the localization of NLOS scenarios is converted into a problem of LOS and the accuracy of localization can be improved greatly compared to other categories due to making full use of the additional information of the scatterers. Therefore, the virtual station method has received immense attention in the research society [26; 27; 28; 29; 30; 31; 32], owing to its great potential for development. The authors of [26] proposed a scattering distance-based (SDB) algorithm using TOA and AOA measurements from a single BS, which estimates iteratively the MS position by estimating the unknown scatterer positions in a prior step, but this algorithm only applies to the case of the ring-of-scatterers (ROS) model, and its performance depends on the accuracy of the initial estimate of the position of MS, which is determined by another localization algorithm in the NLOS environment. In [27], a maneuverable single station is introduced for the NLOS environment. With scatterers regarded as virtual observation stations, the problem of the single-station localization can be converted into a problem of multi-station localization. Whereas the maneuverable BS is an unrealistic assumption in practical scenarios usually, at the same time, this algorithm has high computational complexity or is sensitive to ranging errors. The authors of [28; 29; 30] proposed a geometrical approach to locating virtual stations for the NLOS environments and then located MS based on the positions of virtual stations. However, these algorithms need floor plan information and assume that all reflectors in the scenario are pure and have smooth and regular surfaces, which may be unavailable in practical environments. In [31], pedestrian dead reckoning (PDR) is adopted to construct the virtual station, then the LOS BS and virtual stations participate in the localization of MS with minimum residual. However, when the error of the initial position estimate of MS is large, the algorithm performance will dramatically degrade, meanwhile, PDR is indispensable for the construction of virtual stations, which might limit its applicability in many situations. For any virtual station method, the localization of scatterers is key to the algorithm. On the other hand, as long as the one-bounce scattering signals from the same scatterer are identified, the scatterer can be located accurately with existing LOS algorithms, and then the localization of NLOS scenarios is converted into a problem of LOS easily. Hence, the identification of one-bounce scattering signals from the same scatterer is key to the virtual station method in the NLOS environment. In this paper, we propose a hybrid TOA/AOA virtual station localization algorithm based on scattering signal identification for GNSS-denied urban or indoor NLOS environments. Our main contributions are as follows:  
1. A two-step one-bounce scattering signal identification algorithm used for localization of scatterers is proposed, which does not require any assumption or prior knowledge about MS, BS and scatterer. 2. With scatterers regarded as virtual stations, a virtual station localization algorithm is proposed, which outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy, as far as we know. The remaining of this paper is organized as follows. In Section 2, the first step of the one-bounce scattering signal identification algorithm is described. The second step of the one-bounce scattering signal identification algorithm is presented in Section 3. Section 4 is devoted to the virtual station localization algorithm.