MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Abstract

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF

“…Under such a circumstance, various positioning techniques have been researched, such as IEEE 802.11 WLAN (WiFi) [7,8,9,10], Radio Frequency Identification (RFID) [11], ultrasound [12], Ultra Wideband Beacons (UWB) [13], Bluetooth [14], ZigBee [15], infrared [16], and pseudolites [17]. Sometimes, multiple signal types are utilized simultaneously [18,19]. Since WiFi access points (AP) are installed ubiquitously nowadays, the WiFi approach has been much favored in most cases.…”

Distributed and adaptive location identification system for mobile devices

“…The method requires the positions of access points and positions and properties of the walls. The localization accuracy was in the range of 2 to 4 m. The "multiple frequency adaptive model-based indoor localization method" (MFAM) proposed in [34] was built upon the work in [33] using multiple frequency bands. MFAM was reported to improve the localization performance of its predecessor by 6%.…”