Received Signal Strength-Based Localization Using Delta Method for Non-cooperative Scenario in Wireless Sensor Networks
Summary :
In this letter, we consider the localization problem using received signal strength in wireless sensor networks. Working with a simple non-cooperative scenario in an outdoor localization, we transform the received signal strength measurement model to an alternative optimization problem which is much easier to solve and less complex compared to finding the optimum solutions from the maximum likelihood estimator. Then, we can solve a sequence of nonconvex problems as a range constrainted optimization problem, while the estimated solution also guarantees a monotonic convergence to the original solution. Simulation results confirm the effectiveness of our proposed approach.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.1 pp.450-454
- Publication Date
- 2015/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.450
- Type of Manuscript
- LETTER
- Category
- Communication Theory and Signals
Authors
Thu L.N. NGUYEN
Soongsil University
Yoan SHIN
Soongsil University
Keyword
Latest Issue
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Thu L.N. NGUYEN, Yoan SHIN, "Received Signal Strength-Based Localization Using Delta Method for Non-cooperative Scenario in Wireless Sensor Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 1, pp. 450-454, January 2015, doi: 10.1587/transfun.E98.A.450.
Abstract: In this letter, we consider the localization problem using received signal strength in wireless sensor networks. Working with a simple non-cooperative scenario in an outdoor localization, we transform the received signal strength measurement model to an alternative optimization problem which is much easier to solve and less complex compared to finding the optimum solutions from the maximum likelihood estimator. Then, we can solve a sequence of nonconvex problems as a range constrainted optimization problem, while the estimated solution also guarantees a monotonic convergence to the original solution. Simulation results confirm the effectiveness of our proposed approach.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.450/_p
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@ARTICLE{e98-a_1_450,
author={Thu L.N. NGUYEN, Yoan SHIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Received Signal Strength-Based Localization Using Delta Method for Non-cooperative Scenario in Wireless Sensor Networks},
year={2015},
volume={E98-A},
number={1},
pages={450-454},
abstract={In this letter, we consider the localization problem using received signal strength in wireless sensor networks. Working with a simple non-cooperative scenario in an outdoor localization, we transform the received signal strength measurement model to an alternative optimization problem which is much easier to solve and less complex compared to finding the optimum solutions from the maximum likelihood estimator. Then, we can solve a sequence of nonconvex problems as a range constrainted optimization problem, while the estimated solution also guarantees a monotonic convergence to the original solution. Simulation results confirm the effectiveness of our proposed approach.},
keywords={},
doi={10.1587/transfun.E98.A.450},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - Received Signal Strength-Based Localization Using Delta Method for Non-cooperative Scenario in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 450
EP - 454
AU - Thu L.N. NGUYEN
AU - Yoan SHIN
PY - 2015
DO - 10.1587/transfun.E98.A.450
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2015
AB - In this letter, we consider the localization problem using received signal strength in wireless sensor networks. Working with a simple non-cooperative scenario in an outdoor localization, we transform the received signal strength measurement model to an alternative optimization problem which is much easier to solve and less complex compared to finding the optimum solutions from the maximum likelihood estimator. Then, we can solve a sequence of nonconvex problems as a range constrainted optimization problem, while the estimated solution also guarantees a monotonic convergence to the original solution. Simulation results confirm the effectiveness of our proposed approach.
ER -
English
