Floor determination for positioning in multi-story building

Abstract

WiFi access points (APs) are nowadays ubiquitous in multi-level buildings to provide uninterrupted network access to mobile users. WiFi signals are the widely recognized option to provide feasible indoor positioning system (IPS). However majority of IP research concentrates on 2D positioning compared to vertical or floor level determination. To date the most reliable floor determination techniques based on WiFi signals use fingerprinting approaches. However, fingerprint (FP) method is computationally extensive due to its large database size. This paper proposed a new computationally efficient floor determination system based on reduced database. The performance of proposed algorithm is tested using real-world measurement and is compared against other few available floor determination algorithms. The experiment shows that the proposed method outperforms other floor positioning methods by utilizing only 14% of original FP database size.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…To reduce the computational complexity of floor determination in Wi-Fi, due to the size of the fingerprint database, authors in [152] apply a two-step process, first by rearranging the fingerprints according to unique AP listed by the fingerprint, and second by filtering the unique AP list by selecting only 'significant' APs of the building. The floor detection algorithm based on the reduced database employs Bayesian posterior probability of each floor and is reported to achieve 75% and 86% correct detection rates in two buildings.…”

A Sensor Fusion-Based Framework for Floor Localization

“…Rather than taking all APs into account to develop fingerprints, [26] considered the APs which were accessible from at least two floors. Any APs from which the signals were available from only one floor were discarded from the fingerprints.…”

“…To evaluate the performance, the proposed algorithm was compared with the Nearest Floor algorithm [20]. For the performed experiments in [26], the proposed algorithm achieved 80% floor localization accuracy on average; whereas, on average the Nearest Floor algorithm obtained 65% floor localization accuracy.…”

Floor Detection with Indoor Vertical Positioning in LTE Femtocell Networks

“…Maintenance of the database is also required to ensure its reliability. Still, the computational complexity of floor determination using WiFi fingerprinting databases can be low, such as in [20].…”