Publisher DOI: 10.1109/GLOCOM.2017.8254555
Title: Constrained stochastic inference for cooperative indoor localization
Language: English
Authors: Mendrzik, Rico 
Bauch, Gerhard 
Issue Date: 10-Jan-2018
Source: 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings (2018-January): 1-6 (2018-01-10)
Journal or Series Name: 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings 
Abstract (english): We consider cooperative position estimation in wireless networks as Bayesian inference problems in which nodes with unknown positions (agents) infer their positions based on distance measurements with respect to reference nodes (anchors) and other agents. In the indoor environment, the positions of agents can be constrained to finite geometric sets due to non-negative errors on the range estimates which arise from non-line-of-sight and multipath effects. First, we exploit the non-negativity of ranging errors in order to confine the positions of nodes to convex polygons. Subsequently, we exploit these polygons to relax the inference-based position estimation problems in terms of computational complexity. Using this two-stage approach, we show a tremendous reduction in terms of computational complexity, improvements in terms of localization accuracy, as well as the quicker convergence of the inference algorithm.
ISBN: 978-150905019-2
Institute: Nachrichtentechnik E-8 
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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