LOS Signal Identification Based on Common Chord Intersection Point Position Deviation from MS in Target Localization

Abstract

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.