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      Wi-Fi/MARG Integration for Indoor Pedestrian Localization

      research-article
      , * , , ,
      Sensors (Basel, Switzerland)
      MDPI
      indoor pedestrian localization, Wi-Fi, MARG, PDR, EKPF

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          Abstract

          With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.

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          Most cited references34

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          Nonlinear Complementary Filters on the Special Orthogonal Group

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            Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing.

            R Sabatini (2006)
            In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.
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              Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

              Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                10 December 2016
                December 2016
                : 16
                : 12
                : 2100
                Affiliations
                Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; tianzs@ 123456cqupt.edu.cn (Z.T.); zhoumu@ 123456cqupt.edu.cn (M.Z.); wuzipeng2014@ 123456gmail.com (Z.W.); lizecqupt@ 123456yahoo.com (Z.L.)
                Author notes
                [* ]Correspondence: jinyue063@ 123456gmail.com ; Tel.: +86-23-6246-0295
                Article
                sensors-16-02100
                10.3390/s16122100
                5191080
                27973412
                8b761621-8aa3-4679-91ce-a9b97bd7aecb
                © 2016 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 August 2016
                : 06 December 2016
                Categories
                Article

                Biomedical engineering
                indoor pedestrian localization,wi-fi,marg,pdr,ekpf
                Biomedical engineering
                indoor pedestrian localization, wi-fi, marg, pdr, ekpf

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