Enhanced Indoor Localization System based on Inertial Navigation

Abstract

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues