7 February 2015 Three dimensional indoor positioning based on visible light with Gaussian mixture sigma-point particle filter technique
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Abstract
Over the past decade, location based services (LBS) have found their wide applications in indoor environments, such as large shopping malls, hospitals, warehouses, airports, etc. Current technologies provide wide choices of available solutions, which include Radio-frequency identification (RFID), Ultra wideband (UWB), wireless local area network (WLAN) and Bluetooth. With the rapid development of light-emitting-diodes (LED) technology, visible light communications (VLC) also bring a practical approach to LBS. As visible light has a better immunity against multipath effect than radio waves, higher positioning accuracy is achieved. LEDs are utilized both for illumination and positioning purpose to realize relatively lower infrastructure cost. In this paper, an indoor positioning system using VLC is proposed, with LEDs as transmitters and photo diodes as receivers. The algorithm for estimation is based on received-signalstrength (RSS) information collected from photo diodes and trilateration technique. By appropriately making use of the characteristics of receiver movements and the property of trilateration, estimation on three-dimensional (3-D) coordinates is attained. Filtering technique is applied to enable tracking capability of the algorithm, and a higher accuracy is reached compare to raw estimates. Gaussian mixture Sigma-point particle filter (GM-SPPF) is proposed for this 3-D system, which introduces the notion of Gaussian Mixture Model (GMM). The number of particles in the filter is reduced by approximating the probability distribution with Gaussian components.
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Wenjun Gu, Wenjun Gu, Weizhi Zhang, Weizhi Zhang, Jin Wang, Jin Wang, M. R. Amini Kashani, M. R. Amini Kashani, Mohsen Kavehrad, Mohsen Kavehrad, } "Three dimensional indoor positioning based on visible light with Gaussian mixture sigma-point particle filter technique", Proc. SPIE 9387, Broadband Access Communication Technologies IX, 93870O (7 February 2015); doi: 10.1117/12.2076607; https://doi.org/10.1117/12.2076607
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