Name
Abstract | ||
|---|---|---|
Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applications. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2021.12.074 | Neurocomputing |
Keywords | DocType | Volume |
A-TDOA,Cramèr-Rao bounds,Localization,Local positioning systems,Optimization,Sensor failure,TDOA,Wireless sensor networks | Journal | 484 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javier Diez-Gonzalez | 1 | 0 | 0.34 |
| Ruben Avarez | 2 | 0 | 0.34 |
| Paula Verde | 3 | 0 | 0.34 |
| Ruben Ferrero-Guillen | 4 | 0 | 0.34 |
| Hilde Perez | 5 | 0 | 0.34 |