The Cross-Entropy Method for Maximum Likelihood Location Estimation Based on IEEE 802.15.4 Radio Signals in Sensor Networks
Summary :
This paper considers the problem of target location estimation in a wireless sensor network based on IEEE 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ML) location estimator based on the Cross-Entropy (CE) method. In the proposed CE method, the ML criterion is translated into a stochastic approximation problem which can be solved effectively. Simulations that compare the performance of a ML target estimation scheme employing the conventional Newton method and the conjugate gradient method are presented. The simulation results show that the proposed CE method provides higher location estimation accuracy throughout the sensor field.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.8 pp.2724-2727
- Publication Date
- 2008/08/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e91-b.8.2724
- Type of Manuscript
- LETTER
- Category
- Network
Authors
Keyword
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Jung-Chieh CHEN, "The Cross-Entropy Method for Maximum Likelihood Location Estimation Based on IEEE 802.15.4 Radio Signals in Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 8, pp. 2724-2727, August 2008, doi: 10.1093/ietcom/e91-b.8.2724.
Abstract: This paper considers the problem of target location estimation in a wireless sensor network based on IEEE 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ML) location estimator based on the Cross-Entropy (CE) method. In the proposed CE method, the ML criterion is translated into a stochastic approximation problem which can be solved effectively. Simulations that compare the performance of a ML target estimation scheme employing the conventional Newton method and the conjugate gradient method are presented. The simulation results show that the proposed CE method provides higher location estimation accuracy throughout the sensor field.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.8.2724/_p
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@ARTICLE{e91-b_8_2724,
author={Jung-Chieh CHEN, },
journal={IEICE TRANSACTIONS on Communications},
title={The Cross-Entropy Method for Maximum Likelihood Location Estimation Based on IEEE 802.15.4 Radio Signals in Sensor Networks},
year={2008},
volume={E91-B},
number={8},
pages={2724-2727},
abstract={This paper considers the problem of target location estimation in a wireless sensor network based on IEEE 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ML) location estimator based on the Cross-Entropy (CE) method. In the proposed CE method, the ML criterion is translated into a stochastic approximation problem which can be solved effectively. Simulations that compare the performance of a ML target estimation scheme employing the conventional Newton method and the conjugate gradient method are presented. The simulation results show that the proposed CE method provides higher location estimation accuracy throughout the sensor field.},
keywords={},
doi={10.1093/ietcom/e91-b.8.2724},
ISSN={1745-1345},
month={August},}
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TY - JOUR
TI - The Cross-Entropy Method for Maximum Likelihood Location Estimation Based on IEEE 802.15.4 Radio Signals in Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 2724
EP - 2727
AU - Jung-Chieh CHEN
PY - 2008
DO - 10.1093/ietcom/e91-b.8.2724
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2008
AB - This paper considers the problem of target location estimation in a wireless sensor network based on IEEE 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ML) location estimator based on the Cross-Entropy (CE) method. In the proposed CE method, the ML criterion is translated into a stochastic approximation problem which can be solved effectively. Simulations that compare the performance of a ML target estimation scheme employing the conventional Newton method and the conjugate gradient method are presented. The simulation results show that the proposed CE method provides higher location estimation accuracy throughout the sensor field.
ER -
English
