Abstract
With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL.
Highlights
This paper proposes a method for realizing indoor floor localization using multiintelligent sensors (MIS-IFL)
During the floor localization phase, the corresponding data need to be collected from the sensors in the smartphone, the collected magnetic data are mapped, and the mapped data are matched with the fingerprint database by the Euclidean closest approximation, and the localization result is obtained by the principle of the majority
The results show that the localization accuracy of Wi-Fi-based indoor localization system (WF-ILS) for locating floors is lower than MIS-IFL and MB-ILS, because the localization accuracy of Wi-Fi is highly dependent on the matching of a higher number of access points (APs) with the similarity of Received Signal Strength Indicator (RSSI) values
Summary
Quickly and accurately locating a user’s floor height is critical to saving lives in a fire emergency Navigation services such as Google Maps can prompt mobile users to use floor maps with the assistance of obtaining their current floor level in a shopping mall or an airport. An IFL method based on geomagnetic signal and multiple intelligent sensors is proposed to solve the problems of cumbersome operation and low precision of the existing floor localization method, which uses machine learning classifiers to identify user activity patterns and combines the data from the accelerator sensor, magnetometer sensor and gyro sensor of the smartphone to locate the floors, providing technical support for future indoor localization. The main contributions of the research are briefly summarized as follows: (1) A collaborative approach is proposed based on geomagnetic signal and multiple sensors in a smartphone to achieve target floor localization.
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