Abstract
Wi-Fi based positioning has great potential for use in indoor environments because Wi-Fi signals are near-ubiquitous in many indoor environments. With a Reference Fingerprint Map (RFM), fingerprint matching can be adopted for positioning. Much assisting information can be adopted for increasing the accuracy of Wi-Fi based positioning. One of the most adopted pieces of assisting information is the Pedestrian Dead Reckoning (PDR) information derived from inertial measurements. This is widely adopted because the inertial measurements can be acquired through a Commercial Off The Shelf (COTS) smartphone. To integrate the information of Wi-Fi fingerprinting and PDR information, many methods have adopted filters, such as Kalman filters and particle filters. A new methodology for integration of Wi-Fi fingerprinting and PDR is proposed using graph optimization in this paper. For the Wi-Fi based fingerprinting part, our method adopts the state-of-art hierarchical structure and the Penalized Logarithmic Gaussian Distance (PLGD) metric. In the integration part, a simple extended Kalman filter (EKF) is first used for integration of Wi-Fi fingerprinting and PDR results. Then, the tracking results are adopted as initial values for the optimization block, where Wi-Fi fingerprinting and PDR results are adopted to form an concentrated cost function (CCF). The CCF can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both real-scenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error. Additionally, the proposed method is more robust to outliers in both Wi-Fi based and PDR based results, which is commonly seen in practical situations.
Highlights
While the problem of outdoor positioning has been extensively solved due to the developments of the Global Navigation Satellite System (GNSS), the indoor localization problem is still waiting for a satisfactory and reliable solution
Can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both real-scenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error
The most commonly seen assisted information is the Pedestrian Dead Reckoning (PDR) information because it is normally available from a Commercial Off The Shelf (COTS) smartphone
Summary
While the problem of outdoor positioning has been extensively solved due to the developments of the Global Navigation Satellite System (GNSS), the indoor localization problem is still waiting for a satisfactory and reliable solution. Many techniques have been applied to the area of indoor positioning. According to their different degrees of dependence on external infrastructures, these techniques can roughly be categorized into three types: . This type of positioning method relies on the inertial readings from waist mounted [1,2] or foot mounted [3,4,5] Inertial Measurement Units (IMUs) to calculate the relative position changes of the pedestrian. As the self-contained IMUs are adopted, no external signals or infrastructures are needed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.