Abstract
Network localization and synchronization (NLS) is a paradigm that considers joint inference of positions and clock parameters in a network consisting of completely asynchronous nodes. NLS has the potential to achieve significant performance gains in terms of localization and synchronization accuracy. In this paper, we derive fundamental performance limits of NLS by considering a problem formulation in the non-Bayesian inference framework, in which the waveforms received by different nodes in the network are considered as measurements. We perform equivalent Fisher information analysis to obtain bounds on the accuracy of NLS, and our results reveal how physical parameters and signal departure times affect the inference performance. The analytical results are verified by simulations based on a realistic channel model that takes spatial consistency into consideration.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) |
| Publisher | IEEE |
| ISBN (Print) | 9781538667545 |
| DOIs | |
| State | Published - Jan 17 2019 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2022-06-30Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This research was supported, in part, by the Office of Naval Research under Grant N00014-16-1-2141, by the King Abdullah University of Science and Technology through the Sensor Research Initiative Grant OSR-2015-Sensors-2700, by the Austrian Science Fund (FWF) under grant J3886-N31, and by the MIT Institute for Soldier Nanotechnologies.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.