Geometric-Ransac position estimation method for enhanced indoor positioning using multipath signals for single base station systems

A primary challenge in indoor positioning is multipath signal propagation, which contributes to measurement error, yielding low positioning accuracy. As the demand for location-based services in indoor settings increases, developing accurate indoor positioning systems becomes crucial to accommodating user requirements. Numerous works have proposed methods to mitigate the effect of multipath error on indoor positioning accuracy. However, this issue remains a major topic in the researchers’ discourse. This paper proposes a novel Geometric Random Sample Consensus (Geometric-RANSAC) position estimation method to enhance positioning accuracy in single base station (BS) indoor positioning systems. The method considers the formation of a virtual multiple BS system model by treating each of the multipath components received at a mobile station (MS) position as a signal transmitted by a virtual BS. The position estimation is executed in two phases. In the first phase, the geometric information contained in each multipath component, including the time of arrival, angle of arrival, and elevation of arrival, is extracted to calculate a position estimate. The number of position estimates produced in this phase depends on the number of multipath components contained in the received signal. In the second phase, the RANSAC algorithm with a centroid model is employed to optimize the final MS position. The simulation results show that the proposed method outperforms several least squares method variants by achieving a median positioning error of less than 4.06 cm, and 95% of the MS positions are estimated with an error of less than 7.86 cm. This proposed method not only addresses the challenges posed by multipath propagation but also offers a simple solution for indoor positioning.