TY - GEN
T1 - Fast computation of Cramer-Rao Bounds for TOA
T2 - 2016 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2016
AU - Garzón, Esteban
AU - Valdiviezo, Santiago
AU - Játiva, René
AU - Vidal, Josep
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/23
Y1 - 2017/3/23
N2 - As part of a larger scope work that studies network-based positioning, that employs timing measures, this article proposes a methodology to add Cramer-Rao Bounds (CRBs) information to the propagation model. Moreover, it enables a very quick computation of CRBs for timing, avoiding the growing computational effort resulting from Fisher's matrix formulation and its inversion for each required position at the simulation stage, assuring at the same time the reliability of the required data in the study of positioning using space-time diversity. This methodology considers the variability of the propagation conditions in terms of delay spread (DS) and Signal-to-Noise ratio (SNR) in a realistic scenario. It also coordinates the operation of concurrent models within simulation, and finally performs bi-exponential regression and interpolation procedures on pertinent operational regions for CRBs. Properly validated models provide simple closed expressions that ease the operational region discrimination and its integration to the positioning simulation platform.
AB - As part of a larger scope work that studies network-based positioning, that employs timing measures, this article proposes a methodology to add Cramer-Rao Bounds (CRBs) information to the propagation model. Moreover, it enables a very quick computation of CRBs for timing, avoiding the growing computational effort resulting from Fisher's matrix formulation and its inversion for each required position at the simulation stage, assuring at the same time the reliability of the required data in the study of positioning using space-time diversity. This methodology considers the variability of the propagation conditions in terms of delay spread (DS) and Signal-to-Noise ratio (SNR) in a realistic scenario. It also coordinates the operation of concurrent models within simulation, and finally performs bi-exponential regression and interpolation procedures on pertinent operational regions for CRBs. Properly validated models provide simple closed expressions that ease the operational region discrimination and its integration to the positioning simulation platform.
KW - Antenna Arrays
KW - Bi-exponential regression
KW - Cramer Rao Bounds (CRB)
KW - Greenstein model
KW - Time Of Arrival (TOA)
KW - network-based positioning
KW - wireless sensor networks
UR - https://www.scopus.com/pages/publications/85018191145
U2 - 10.1109/LA-CCI.2016.7885716
DO - 10.1109/LA-CCI.2016.7885716
M3 - Contribución a la conferencia
AN - SCOPUS:85018191145
T3 - 2016 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2016 - Proceedings
BT - 2016 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2016 - Proceedings
A2 - Rodriguez, Cristian
A2 - Gomez, Juan Bernardo
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 November 2016 through 4 November 2016
ER -