Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization

Bruno Betoni Parodi, Henning Lenz, Andrei Szabo, Hui Wang, Joachim Horn, Joachim Bamberger, and Dragan Obradovic

Abstract:	Common approaches for indoor positioning based on cellular communication systems use as measurements the received signal strength (RSS). In order to work properly, such a system often requires many calibration points before its start. This paper presents a two-fold approach achieving high indoor localization accuracies without requiring too many calibration points. The basic idea is to use an initial propagation model with few parameters, which can be adapted by a few measurements, e.g. mutual measurements of access points. Then the model is refined by incorporating additional parameters and using online learning. Investigations on the requirements and potentials of different approaches and results for DECT and WLAN setups are given. The first approach uses predefined paths that should be passed through by a service technician with measurement equipment. The second approach uses a Kohonen-like learning algorithm to adapt the model on-the-fly. For both approaches linear propagation models and more involved dominant path models incorporating map information are applied for the initialization.
Published in:	Proceedings of IEEE/ION PLANS 2006 April 25 - 27, 2006 Loews Coronado Resort Hotel San Diego, CA
Pages:	164 - 172
Cite this article:	Parodi, Bruno Betoni, Lenz, Henning, Szabo, Andrei, Wang, Hui, Horn, Joachim, Bamberger, Joachim, Obradovic, Dragan, "Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization," Proceedings of IEEE/ION PLANS 2006, San Diego, CA, April 2006, pp. 164-172. https://doi.org/10.1109/PLANS.2006.1650600
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