An Adaptive Fingerprint Database Updating Method for Room Localization

Abstract

Fingerprint-based indoor localization has become one of the most attractive and promising techniques, and however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially for the large-scale and long-term deployment. In this paper, focusing on room localization, we first analyze the effect of different factors on indoor location accuracy, then propose an integrity check algorithm and a fuzzy map mechanism in response to network changes accurately and timely, and then design an accuracy check algorithm and a regional adaptive periodic update approach to update the fingerprint database effectively when an environment changes. Meanwhile, we design the active static data collecting mechanism and the active pin data collecting mechanism to ensure the accuracy and reliability of fingerprints newly captured and reduce the overhead on mobile devices significantly. The experimental results demonstrate that the proposed solution improves the performance of updating the fingerprint database in real time and robustness and maintains the location accuracy over 95% no matter how the indoor environmental changes; meanwhile, it also reduces energy efficiency imposed on the mobile phones by over 50%.

A REM Update Methodology Based on Clustering and Random Forest

“…For indoor localization systems, mobile crowd sensing has become a popular method for updating radio maps [5][6][7][8][9][10][11][12]. In [5][6][7], the authors propose an approach where mobile users generate reports at their current locations to update the radio maps.…”

“…The method to update the radio map is based on simply appending the new fingerprints into the database and removing those that are older than a certain period. In [8], the authors use an integrity check algorithm to determine whether to update the radio map used for indoor localization. In this method, several new fingerprints are accumulated before updating the received signal strength indicator (RSSI) value at a particular location.…”

Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

“…To implement a practical indoor localization system, the mechanism to deal with the change of the radio environment is a necessity. The evolution of the radio environment (for example adding or removing the access points) can be examined by looking at the signal variance at similar locations, as shown in [63] [64] [65] [66] [67]. Some MAC addresses might be static for a fixed duration of time, but are essentially mobile for example personal hotspots.…”

An Enhanced Indoor Three-Dimensional Localization System with Sensor Fusion Based on Ultra-Wideband Ranging and Dual Barometer Altimetry

“…However, with the advancement of indoor three-dimensional (3D) localization sensor technology, emerging services like indoor drone control [ 2 , 13 ], virtual reality (VR) [ 14 , 15 ], or augmented reality (AR) [ 16 ] experiences demand higher accuracy in location. They also require more stringent height estimation and more frequent updates of information [ 17 , 18 ]. Additionally, while exploring the potential of these technologies, it is important to consider the cost factor [ 19 ] to ensure that technological solutions have the potential for widespread application.…”