Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods
AbstractThe accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain. View Full-Text
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Álvarez, R.; Díez-González, J.; Alonso, E.; Fernández-Robles, L.; Castejón-Limas, M.; Perez, H. Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods. Sensors 2019, 19, 3024.
Álvarez R, Díez-González J, Alonso E, Fernández-Robles L, Castejón-Limas M, Perez H. Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods. Sensors. 2019; 19(13):3024.Chicago/Turabian Style
Álvarez, Rubén; Díez-González, Javier; Alonso, Efrén; Fernández-Robles, Laura; Castejón-Limas, Manuel; Perez, Hilde. 2019. "Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods." Sensors 19, no. 13: 3024.
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