Indoor positioning based on ranging offset model and learning

The performance of a range-based indoor positioning system is severely degraded by non-line-of-sight (NLOS) propagation due to the offsets in range measurements (i.e., NLOS errors). It is difficult to predict or mitigate the NLOS errors since they vary from one position to another due to changes in the environment. In this paper, we propose a novel NLOS error mitigation scheme based on the integration of an Inertial Measurement Unit (IMU) and learning of the application environment. We first propose a location-dependent ranging offset model to characterize the NLOS errors. An iterative algorithm is then proposed to jointly estimate the trajectory of an IMU-equipped mobile node and learn the location-dependent ranging offset model in the application environment. The performance of the proposed scheme is validated experimentally using an indoor positioning system. It is shown that the median positioning error of an IMU-equipped node is reduced by 92% using the proposed algorithm compared with using a conventional real time tracking algorithm. In addition, the real-time positioning error of a mobile node without IMU can be reduced by 86% if the learned ranging offset model is used for NLOS error mitigation.