Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

Abstract

Indoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix-based RSSI indoor positioning (M-RIP) for initial location estimation and feature-based image indoor positioning (F-IIP) for position determination via image feature matching. Furthermore, extended area-based post-processing (EA-PP) is employed to augment M-RIP's precision and minimize image matching computation in F-IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.