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      A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS

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          Abstract

          Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.

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          Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning

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            Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

            The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.
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              iParking: An Intelligent Indoor Location-Based Smartphone Parking Service

              Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                December 2012
                12 December 2012
                : 12
                : 12
                : 17208-17233
                Affiliations
                Department of Navigation and Positioning, Finnish Geodetic Institute, Geodeetinrinne 2, Masala 02431, Finland; E-Mails: ruizhi.chen@ 123456fgi.fi (R.C.); ling.pei@ 123456fgi.fi (L.P.); robert.guinness@ 123456fgi.fi (R.G.); heidi.kuusniemi@ 123456fgi.fi (H.K.)
                Author notes
                [* ] Author to whom correspondence should be addressed; E-Mail: jingbin.liu@ 123456fgi.fi ; Tel.: +358-9-2955-5313; Fax: +358-9-2955-5200.
                Article
                sensors-12-17208
                10.3390/s121217208
                3571834
                23235455
                87aa6d65-0ec5-414f-8ac0-2cac7f91b8cf
                © 2012 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 license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 29 October 2012
                : 06 December 2012
                : 07 December 2012
                Categories
                Article

                Biomedical engineering
                smartphone positioning,mobile lbs,probabilistic algorithms,sensor fusion,ubiquitous computing

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