WIFI Fingerprint Correction for Indoor Localization Based on Distance Metric Learning

Over the past few years, WIFI indoor localization has attracted a lot of attention, particularly due to people spending increasing amounts of time indoors. Nonetheless, achieving accurate indoor localization across multiple floors remains challenging due to signal fluctuations. Consequently, inconsistencies arise between the physical distance and the received signal strength indicator (RSSI) distance among different points. Moreover, in multifloor environments, the received RSSI fingerprint generates a high-dimensional sparse matrix which results in high computational costs. This article presents a novel multifloor indoor localization method called distance metric learning K-nearest neighbors (DML-KNNs) localization aiming to enhance floor identification rate and localization accuracy. To mitigate the effects of signal fluctuations, we propose a novel method for correcting the RSSI fingerprint with distance metric learning (DML). This method improves the accuracy of the K-nearest neighbor (KNN) algorithm, both in the floor classification phase and localization phase. The simulation and experimental results demonstrate that our proposed DML-KNN method achieves high localization accuracy.