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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Laaraiedh, M. Avrillon, S. Uguen, B. |
| Copyright Year | 2009 |
| Description | Author affiliation: IETR, University of Rennes 1, France (Laaraiedh, M.; Avrillon, S.; Uguen, B.) |
| Abstract | In this paper, the problem of uncertainty on reference nodes positions is addressed in the context of hybrid data fusion techniques for localization. This problem arises in B3G networks where different location-dependent observables come from heterogeneous Radio Access Networks (RAN) leading to different levels of uncertainty on both ranges and anchor nodes positions. We assume a Gaussian model on the node position error as well as on the ranging error. We derive novel Maximum Likelihood based location estimator which considers these two sources of uncertainty. The performances of this new estimator is then compared to the ML estimator which does not consider erroneous reference nodes positions. Monte Carlo simulations show that the proposed estimator achieves better performances especially in the context of short range positioning. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 954544 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424443970 |
| DOI | 10.1109/ICSCS.2009.5412689 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-06 |
| Publisher Place | Tunisia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Performance evaluation Imperfect Reference Positions Maximum likelihood estimation Uncertainty Circuits and systems ToA Maximum Likelihood Optimization Radio access networks Degradation Wireless sensor networks RSS Hybrid Data Fusion Position measurement Radio propagation Resource management Localization |
| Content Type | Text |
| Resource Type | Article |
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