2013
Valera, Isabel; Sieskul, Bamrung; Miguez, Joaquin
On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent Artículo de revista
En: EURASIP Journal on Wireless Communications and Networking, vol. 2013, no 1, pp. 158, 2013, ISSN: 1687-1499.
Resumen | Enlaces | BibTeX | Etiquetas: Maximum likelihood estimator, Path loss exponent, Time-of-arrival estimation
@article{Valera2013,
title = {On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent},
author = {Isabel Valera and Bamrung Sieskul and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P37_2013_On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent.pdf
http://jwcn.eurasipjournals.com/content/2013/1/158},
issn = {1687-1499},
year = {2013},
date = {2013-01-01},
journal = {EURASIP Journal on Wireless Communications and Networking},
volume = {2013},
number = {1},
pages = {158},
publisher = {Springer},
abstract = {We investigate the estimation of the time of arrival (ToA) of a radio signal transmitted over a flat-fading channel. The path attenuation is assumed to depend only on the transmitter-receiver distance and the path loss exponent (PLE) which, in turn, depends on the physical environment. All previous approaches to the problem either assume that the PLE is perfectly known or rely on estimators of the ToA which do not depend on the PLE. In this paper, we introduce a novel analysis of the performance of the maximum likelihood (ML) estimator of the ToA under an imperfect knowledge of the PLE. Specifically, we carry out a Taylor series expansion that approximates the bias and the root mean square error of the ML estimator in closed form as a function of the PLE error. The analysis is first carried out for a path loss model in which the received signal gain depends only on the PLE and the transmitter-receiver distance. Then, we extend the obtained results to account also for shadow fading scenarios. Our computer simulations show that this approximate analysis is accurate when the signal-to-noise ratio (SNR) of the received signal is medium to high. A simple Monte Carlo method based on the analysis is also proposed. This technique is computationally efficient and yields a better approximation of the ML estimator in the low SNR region. The obtained analytical (and Monte Carlo) approximations can be useful at the design stage of wireless communication and localization systems.},
keywords = {Maximum likelihood estimator, Path loss exponent, Time-of-arrival estimation},
pubstate = {published},
tppubtype = {article}
}
We investigate the estimation of the time of arrival (ToA) of a radio signal transmitted over a flat-fading channel. The path attenuation is assumed to depend only on the transmitter-receiver distance and the path loss exponent (PLE) which, in turn, depends on the physical environment. All previous approaches to the problem either assume that the PLE is perfectly known or rely on estimators of the ToA which do not depend on the PLE. In this paper, we introduce a novel analysis of the performance of the maximum likelihood (ML) estimator of the ToA under an imperfect knowledge of the PLE. Specifically, we carry out a Taylor series expansion that approximates the bias and the root mean square error of the ML estimator in closed form as a function of the PLE error. The analysis is first carried out for a path loss model in which the received signal gain depends only on the PLE and the transmitter-receiver distance. Then, we extend the obtained results to account also for shadow fading scenarios. Our computer simulations show that this approximate analysis is accurate when the signal-to-noise ratio (SNR) of the received signal is medium to high. A simple Monte Carlo method based on the analysis is also proposed. This technique is computationally efficient and yields a better approximation of the ML estimator in the low SNR region. The obtained analytical (and Monte Carlo) approximations can be useful at the design stage of wireless communication and localization systems.
2010
Valera, Isabel; Sieskul, B T; Zheng, F; Kaiser, T
A Hybrid SS-ToA Wireless Ge- olocation Based on Path Attenuation under Imperfect Path Loss Exponent Proceedings Article
En: 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, 2010.
Resumen | Enlaces | BibTeX | Etiquetas: hood estimator, maximum likeli-, Path loss exponent, Time-of-arrival estimation
@inproceedings{Valera2010,
title = {A Hybrid SS-ToA Wireless Ge- olocation Based on Path Attenuation under Imperfect Path Loss Exponent},
author = {Isabel Valera and B T Sieskul and F Zheng and T Kaiser},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569292415.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {18th European Signal Processing Conference (EUSIPCO-2010)},
address = {Aalborg},
abstract = {We consider the wireless geolocationusing the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the path loss exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator un- der the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numer- ical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the im- perfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML es- timator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outper- form the ML estimator under the imperfect PLE.},
keywords = {hood estimator, maximum likeli-, Path loss exponent, Time-of-arrival estimation},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the wireless geolocationusing the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the path loss exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator un- der the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numer- ical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the im- perfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML es- timator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outper- form the ML estimator under the imperfect PLE.