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Accurate localization is the premise of many technologies and applications, such as navigation, emergency assistance and wireless sensor network. For Global Navigation Satellite System (GNSS)-denied urban or indoor environments, various localization technologies based on mobile communication networks or other wireless technologies have been designed and developed. The main challenge for these localization technologies is the presence of a non-line-of-sight (NLOS) propagation environment due to dense obstacles or buildings. The virtual station method is a promising high-accuracy target localization technique in NLOS environments, and the localization of the scatterer is key to the virtual station method. Once one-bounce scattering signals from the same scatterer are identified, the localization of the scatterer can be achieved easily with the existing localization algorithm of line-of-sight (LOS) scenario, and then the localization of NLOS scenarios is converted into a problem of LOS easily. In this paper, a hybrid time of arrival (TOA)/angle of arrival (AOA) virtual station localization algorithm based on scattering signal identification is proposed. Firstly, one-bounce scattering signals from the same scatterer are identified based on TOA/AOA measurements. Next, scatterers are located based on one-bounce scattering signals with the LOS localization algorithm, and then scatterers are regarded as virtual stations and used for mobile station (MS) localization. Compared with the existing research on the virtual station method, the proposed algorithm relies only on TOA/AOA measurements and does not require any assumption or prior knowledge about the scatterer, base station (BS) or MS, which provides a solid foundation for feasible target localization. Simulation results demonstrate, as far as we know, the proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy.

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Target localization has been a popular research topic in recent years since it is the basis of all kinds of location-based applications. For GNSS-denied urban or indoor environments, the localization method based on time-of-arrival (TOA) is one of the most popular localization methods due to its high accuracy and simplicity. However, the Non-line-of-sight (NLOS) error is the major cause that degrades the accuracy of the TOA-based localization method. Identifying whether a received signal at a base station (BS) is due to a line-of-sight (LOS) transmission or NLOS is the key to TOA-based localization methods. In the popular LOS signal identification methods, compared with statistic signal methods and machine learning methods, the geometric constraint method has the advantages of simplicity and without requiring priori knowledge of signals and large amounts of training datasets. In this paper, we propose a geometric constraint two-step LOS signal identification method based on common chord intersection point position deviation from mobile stations (MS). 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 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, based on mutual distance threshold and discrimination result matrix, common chord intersection points of LOS BS combination, and corresponding 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. Compared with the other two geometric constraint methods, the proposed algorithm has better identification accuracy, and the setting of the identification threshold value has a theoretical basis, which facilitates the application of the proposed algorithm.

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated.