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Open AccessArticle

Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments

1
Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy, Via Diocleziano 328, Napoli 80124, Italy
2
Faculty of Engineering and Information Sciences, University ofWollongong in Dubai, Block 15, Dubai Knowledge Park, 20183 Dubai, UAE
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2469; https://doi.org/10.3390/s17112469
Received: 21 September 2017 / Revised: 22 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
(This article belongs to the Section Physical Sensors)
Indoor positioning of mobile devices plays a key role in many aspects of our daily life. These include real-time people tracking and monitoring, activity recognition, emergency detection, navigation, and numerous location based services. Despite many wireless technologies and data-processing algorithms have been developed in recent years, indoor positioning is still a problem subject of intensive research. This paper deals with the active radio-frequency (RF) source localization in indoor scenarios. The localization task is carried out at the physical layer thanks to receiving sensor arrays which are deployed on the border of the surveillance region to record the signal emitted by the source. The localization problem is formulated as an imaging one by taking advantage of the inverse source approach. Different measurement configurations and data-processing/fusion strategies are examined to investigate their effectiveness in terms of localization accuracy under both line-of-sight (LOS) and non-line of sight (NLOS) conditions. Numerical results based on full-wave synthetic data are reported to support the analysis. View Full-Text
Keywords: data fusion; inverse source; RF localization data fusion; inverse source; RF localization
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MDPI and ACS Style

Gennarelli, G.; Al Khatib, O.; Soldovieri, F. Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments. Sensors 2017, 17, 2469.

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