Abstract
For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings.
Highlights
Nowadays, as many people spend about 80% of their life indoors [1], indoor location is becoming more and more important to our daily life
MAC addresses based on offline clustering results; secondly, precise positioning is achieved by using positioning is implemented by matching MAC addresses based on offline clustering results; the similarity matching between the real-time Received Signal Strength Indicator (RSSI) value vector of the receivers and those pre-stored in secondly, precise positioning is achieved by using the similarity matching between the real-time the fingerprint database [25]
Ati ; Rtij and Mtij denote respectively the top m of RSSIs and corresponding MAC addresses received by reference point Ati ; j ranges from 1 to m with the maximum of 10, that is to say, m is 10; Ft is the floor number
Summary
As many people spend about 80% of their life indoors [1], indoor location is becoming more and more important to our daily life. Indoor positioning methods based on sensors, e.g., PDR The multi-sensor fusion localization method, such as WiFi-PDR (Pedestrian Dead Reckoning) indoor positioning, has become a new trend [9], but the heterogeneity of all kinds of sensors makes the fusion calculation challenging. Deng et al [11] proposed a WLAN indoor floor identification method based on the k-means algorithm that classifies floors according to the relationship between RSSI value and distance and takes the nearest class of subordinate floors as the final result. Alex et al [12] proposed another method based on GSM (global system for mobile communications) fingerprint to identify the floor and studied different methods to reduce the amount of fingerprint collection effectively under the condition of ensuring a high identification accuracy rate. MAC addresses and RSSI from WLAN in multi-floor indoor environment are collected and stored into fingerprint database.
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