Method of Reducing Search Area for Localization in Sensor Networks

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