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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Morelli, C. Nicoli, M. Rampa, V. Spagnolini, U. Alippi, C. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dipt. Elettronica e Inf., Politecnico di Milano, Milan (Morelli, C.; Nicoli, M.) |
| Abstract | This paper focuses on the development of a radio localization technique for a wireless sensor network infrastructure where a large number of simple power-aware nodes are spread in indoor environments. Fixed and moving nodes exchange radio messages but can only measure mutual power figures such as the received signal strength (RSS) indicator. Local maximum likelihood estimation from propagation models suffers from false alarm problems due to incorrect position information, complex indoor propagation effects and simple hardware radio architectures. Here, we propose a Bayesian approach to estimate and track the position of a moving node from power maps obtained through field measurements. To lower the computational power required by grid-based algorithms, we exploit particle filter techniques that implement an irregular sampling of the a-posteriori probability space. Finally, experimental results are presented and discussed |
| Sponsorship | The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS) |
| File Size | 361283 |
| File Format | |
| ISBN | 142440469X |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2006.1661129 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-05-14 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle filters Wireless sensor networks Power measurement Indoor environments Maximum likelihood estimation Hardware Computer architecture Bayesian methods Position measurement Grid computing |
| Content Type | Text |
| Resource Type | Article |
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