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| Title | Rf-Msip: Radio Frequency Multi-Source Indoor Positioning |
|---|---|
| ID_Doc | 46615 |
| Authors | Perekadan V.; Mukherjee T.; Banerjee C.; Pasiliao E. |
| Year | 2019 |
| Published | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
| DOI | http://dx.doi.org/10.1109/BigData47090.2019.9006085 |
| Abstract | Computation of accurate indoor positioning information is important in several areas like mobile robotics, large scale sensor networks, smart city, virtual reality and applications involving internet-of-things (IoT). In spite of its growing importance due to the advent of large scale autonomous deployments of sensor networks and IoTs, we still do not have a solution for indoor positioning that is equivalent to the Global Positioning System (GPS), which is the standard for large scale outdoor 10- calization. However GPS is not useful for indoor positioning as it is often unreliable and/or unavailable in indoor environments. In this paper we present a multi-source radio frequency (RF) based framework for automatic indoor positioning using received signal strength (RSS) and demonstrate its efficacy by simultaneously using broadcast FM radio GSM signals for position estimation. Our framework is data driven and can be justified using Bayesian minimum risk analysis and is easy to extend by incorporating other sources of RF signals (like WiFi signals) and thus provides a generalized framework for building indoor positioning systems using signals of opportunity. We call our framework RF-MSiP: Radio Frequency Multi-Source Indoor Positioning Using our algorithms with the well known AMBILOC dataset, we can localize exactly for approximately 98.7% of the test locations over a period of one year, which demonstrates not only the efficacy of the algorithms but also its resiliency to change in the RF environment across the year, thus establishing the transfer learning capability of our system. © 2019 IEEE. |
| Author Keywords | Bayesian Inference; Indoor; IoT; Multi-Source.; Positioning; Received Signal Strength; Sensors; Transfer Learning |
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