TY - GEN
T1 - Method of reducing search area for localization in sensor networks
AU - Shirahama, Junichi
AU - Ohtsuki, Tomoaki
AU - Kaneko, Toshinobu
PY - 2006/12/1
Y1 - 2006/12/1
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
T3 - IEEE Vehicular Technology Conference
SP - 354
EP - 357
BT - 2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring - Proceedings
T2 - 2006 IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring
Y2 - 7 May 2006 through 10 July 2006
ER -