Abstract
Pedestrian dead reckoning (PDR) positioning algorithms can be used to obtain a target’s location only for movement with step features and not for driving, for which the trilateral Bluetooth indoor positioning method can be used. In this study, to obtain the precise locations of different states (pedestrian/car) using the corresponding positioning algorithms, we propose an adaptive method for switching between the PDR and car indoor positioning algorithms based on multilayer time sequences (MTSs). MTSs, which consider the behavior context, comprise two main aspects: filtering of noisy data in small-scale time sequences and using a state chain to reduce the time delay of algorithm switching in large-scale time sequences. The proposed method can be expected to realize the recognition of stationary, walking, driving, or other states; switch to the correct indoor positioning algorithm; and improve the accuracy of localization compared to using a single positioning algorithm. Our experiments show that the recognition of static, walking, driving, and other states improves by 5.5%, 45.47%, 26.23%, and 21% on average, respectively, compared with convolutional neural network (CNN) method. The time delay decreases by approximately 0.5–8.5 s for the transition between states and by approximately 24 s for the entire process.
Highlights
In recent years, the importance of mobile devices, smartphones, has increased for indoor positioning
Wi-Fi, Radio Frequency Identification (RFID), Bluetooth, Ultra Wideband (UWB), pedestrian dead reckoning (PDR), and a few other technologies are widely used on the basis of existing deployment environments or different targets [2]
We propose a multilayer time sequence (MTS) method that combines the positioning information of an indoor environment to improve the accuracy of recognition or to remove noise and switch to the correct algorithm according to the current state
Summary
The importance of mobile devices, smartphones, has increased for indoor positioning. The widespread use of these devices enables location-based services (LBSs) to be made available [1] for every user. This makes indoor positioning and navigation more realizable. Accurate user localization, which is regarded as an essential component of LBSs, can be provided using the embedded sensors and modules of smartphones [1]. The inner sensors of mobile phones are used widely to assist with localization in indoor parking, as they overcome the problem of weak signals in indoor environments and provide a convenient method for users to park. Current indoor positioning approaches are already quite mature owing to these technologies
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