Method of reducing search area for localization in sensor networks

Junichi Shirahama, Tomoaki Ohtsuki, Toshinobu Kaneko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-Ratios Source Localization Nonlinear Least Square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm.

Original languageEnglish
Title of host publicationIEEE Vehicular Technology Conference
Pages354-357
Number of pages4
Volume1
DOIs
Publication statusPublished - 2006
Event2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring - Melbourne, Australia
Duration: 2006 May 72006 Jul 10

Other

Other2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring
CountryAustralia
CityMelbourne
Period06/5/706/7/10

Fingerprint

Sensor networks
Maximum likelihood
Maximum likelihood estimation
Sensors
Mean square error
Monitoring

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Shirahama, J., Ohtsuki, T., & Kaneko, T. (2006). Method of reducing search area for localization in sensor networks. In IEEE Vehicular Technology Conference (Vol. 1, pp. 354-357). [1682835] https://doi.org/10.1109/VETECS.2005.1543310

Method of reducing search area for localization in sensor networks. / Shirahama, Junichi; Ohtsuki, Tomoaki; Kaneko, Toshinobu.

IEEE Vehicular Technology Conference. Vol. 1 2006. p. 354-357 1682835.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shirahama, J, Ohtsuki, T & Kaneko, T 2006, Method of reducing search area for localization in sensor networks. in IEEE Vehicular Technology Conference. vol. 1, 1682835, pp. 354-357, 2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring, Melbourne, Australia, 06/5/7. https://doi.org/10.1109/VETECS.2005.1543310
Shirahama J, Ohtsuki T, Kaneko T. Method of reducing search area for localization in sensor networks. In IEEE Vehicular Technology Conference. Vol. 1. 2006. p. 354-357. 1682835 https://doi.org/10.1109/VETECS.2005.1543310
Shirahama, Junichi ; Ohtsuki, Tomoaki ; Kaneko, Toshinobu. / Method of reducing search area for localization in sensor networks. IEEE Vehicular Technology Conference. Vol. 1 2006. pp. 354-357
@inproceedings{035cfb10014646c2b463aa0257464c21,
title = "Method of reducing search area for localization in sensor networks",
abstract = "One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-Ratios Source Localization Nonlinear Least Square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm.",
author = "Junichi Shirahama and Tomoaki Ohtsuki and Toshinobu Kaneko",
year = "2006",
doi = "10.1109/VETECS.2005.1543310",
language = "English",
isbn = "0780388879",
volume = "1",
pages = "354--357",
booktitle = "IEEE Vehicular Technology Conference",

}

TY - GEN

T1 - Method of reducing search area for localization in sensor networks

AU - Shirahama, Junichi

AU - Ohtsuki, Tomoaki

AU - Kaneko, Toshinobu

PY - 2006

Y1 - 2006

N2 - One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-Ratios Source Localization Nonlinear Least Square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm.

AB - One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-Ratios Source Localization Nonlinear Least Square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm.

UR - http://www.scopus.com/inward/record.url?scp=34047111792&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34047111792&partnerID=8YFLogxK

U2 - 10.1109/VETECS.2005.1543310

DO - 10.1109/VETECS.2005.1543310

M3 - Conference contribution

AN - SCOPUS:34047111792

SN - 0780388879

SN - 0780393929

SN - 9780780393929

VL - 1

SP - 354

EP - 357

BT - IEEE Vehicular Technology Conference

ER -