Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

Abstract

Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.

Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

“…The sampling rate of the device to the received signal is 100 Hz, and relevant data collected by the RPs usually includes Wi-Fi signal strength and the real coordinates of RPs. The received signal strength is an integer in (´110,´15); 390 efficient points are finally collected from the fingerprint point distribution, as shown in Figure 2 [34]. From the figure, we can see that the signal propagation was stable from 23:00 to 7:00 but fluctuated greatly from 7:00 to 23:00.…”

İç mekân konum tespitinde sinyal haritasının küçültülmesi ve performans analizi

“…Düzensiz formda ise alan üzerinde rastgele noktalardan ölçüm yapılır [9]. Mekân içeriside yürünerek veriler toplanır [10]. Ancak her iki yaklaşımda da sinyal haritasının oluşturulması zorlu ve karmaşık bir süreçtir.…”

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

“…In short, these RSS-based approaches are not suitable to implement on smartphones, either because of their low accuracy, or because of complicated preprocessing. Recent research tends to combine both PDR and RSS-based techniques to achieve a better performance [5,6,7]. Indoor map information is also taken into consideration among some of these studies [8,9].…”