Method of reducing search area for localization in sensor networks

Junichi Shirahama, Tomoaki Ohtsuki, Toshinobu Kaneko

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring - Proceedings
ページ354-357
ページ数4
DOI
出版ステータスPublished - 2006 12 1
イベント2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring - Melbourne, Australia
継続期間: 2006 5 72006 7 10

出版物シリーズ

名前IEEE Vehicular Technology Conference
1
ISSN(印刷版)1550-2252

Other

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

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

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