Abstract
Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment.
Highlights
IntroductionGeneration techniques, indoor positioning and navigation have gained significant attention, since the signals from the widely-used global navigation satellite systems are often
In the recent decade, generation techniques, indoor positioning and navigation have gained significant attention, since the signals from the widely-used global navigation satellite systems are oftenMicromachines 2015, 6 not hearable inside buildings
The velocity and heading data obtained from the MEMS sensors, v and φ, together with the results of WiFi fingerprinting-based positioning are set as the input data of the robust extended Kalman filter (EKF)
Summary
Generation techniques, indoor positioning and navigation have gained significant attention, since the signals from the widely-used global navigation satellite systems are often. The indoor positioning and navigation approaches generally require higher accuracy and better adaptation to the environment compared to outdoor ones. Under such a circumstance, a number of positioning techniques have been studied and even used in many special scenarios, like Bluetooth, ultra-wide band (UWB), radio frequency identification (RFID) and. Measurements and, thereby, deteriorate the accuracy of fingerprinting-based positioning To solve this problem, some previous positioning techniques by integrating WiFi fingerprinting and MEMS sensors are proposed in [10,11,12,13,14].
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