Weighted centroid localization (WCL) based on received signal strength measurements is an attractive lowcomplexity solution that enables sensor networks to have a geolocation awareness of the radio environment. In this letter, we propose an analytical framework to calculate the performance of WCL, combining the Taylor series expansion of the logarithm of the estimation error and Jensen’s inequality. In particular, we derive easy-to-handle expressions of the root mean square error and bias of both two- and one-dimensional localization errors without involving any integral. The proposed approach can tackle scenarios with independent and identically distributed shadowing as well as correlated shadowing. Numerical results confirm that the proposed framework is capable of predicting the performance of WCL remarkably well.
Kagiso Magowe, A.G. (2019). Closed-Form Approximation of Weighted Centroid Localization Performance. IEEE SENSORS LETTERS, 3(12), 1-4 [10.1109/LSENS.2019.2948585].
Closed-Form Approximation of Weighted Centroid Localization Performance
Andrea GiorgettiMembro del Collaboration Group
;
2019
Abstract
Weighted centroid localization (WCL) based on received signal strength measurements is an attractive lowcomplexity solution that enables sensor networks to have a geolocation awareness of the radio environment. In this letter, we propose an analytical framework to calculate the performance of WCL, combining the Taylor series expansion of the logarithm of the estimation error and Jensen’s inequality. In particular, we derive easy-to-handle expressions of the root mean square error and bias of both two- and one-dimensional localization errors without involving any integral. The proposed approach can tackle scenarios with independent and identically distributed shadowing as well as correlated shadowing. Numerical results confirm that the proposed framework is capable of predicting the performance of WCL remarkably well.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.