2012 IEEE Globecom Workshops 2012
DOI: 10.1109/glocomw.2012.6477760
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State estimation and motion tracking for spatially diverse VLC networks

Abstract: Improvements in solid-state/LED lighting are driving increased capabilities of lighting systemsoffering potential for applications such as Visible Light Communications (VLC) and indoor localization using the lighting medium. In this paper, we motivate the adoption of localization for both the support of handover between arrays of VLC-equipped luminaires as well as for indoor positioning and beam steering. Our approach uses a state estimation model to achieve localization and motion tracking in spatially divers… Show more

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Cited by 32 publications
(14 citation statements)
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“…Because of the many advantages of LEDs, there are an increasing number of papers published describing a variety of schemes for indoor positioning using LEDs [12]- [18]. The ranging techniques used in these papers include received signal strength (RSS) [12]- [14], [17], [18], time difference of arrival (TDOA) [15], and phase difference of arrival (PDOA) [16]. Most of the papers published so far have presented either theoretical analyses or simulation results for very specific idealized scenarios in which parameters such as the optical power transmitted and the radiation pattern of each LED are precisely known.…”
mentioning
confidence: 99%
“…Because of the many advantages of LEDs, there are an increasing number of papers published describing a variety of schemes for indoor positioning using LEDs [12]- [18]. The ranging techniques used in these papers include received signal strength (RSS) [12]- [14], [17], [18], time difference of arrival (TDOA) [15], and phase difference of arrival (PDOA) [16]. Most of the papers published so far have presented either theoretical analyses or simulation results for very specific idealized scenarios in which parameters such as the optical power transmitted and the radiation pattern of each LED are precisely known.…”
mentioning
confidence: 99%
“…The general form of Kalman filter can be derived from [6], where the linear state vector is x, refers to the coordinates velocities in space and y refers to the received powers from A through E and an external source at F. Considering process and observation noises to be independent, zero-mean, Gaussian white noises, the general state and observation equations can be represented as [ 1] [ ] [ ] t t w t + = + x Tx (3) [ ]…”
Section: State Model and Trackingmentioning
confidence: 99%
“…Our state space model implementation is based on the extension from [3], where transmitters and receiver remain stationary in z dimension; in our case, we assume that the transmitters remain stationary in 3-D and the receiver is free to move in all directions. The model is defined in equations (3), (4), (5) and (6).…”
Section: Icton 2015mentioning
confidence: 99%
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“…the received signal strength (RSS) [11], [12], [13], the angle-of-arrival (AOA) [14], [15] or the time-of-arrival (TOA) [16], [17]. Among these techniques, we distinguish the approaches that make use of the knowledge of the radiation pattern of the LEDs, i.e.…”
mentioning
confidence: 99%