Data fusion based on RBF and nonparametric estimation for localization in Wireless Sensor Networks

Abstract

Localization is one of important functions in Wireless Sensor Networks (WSNs). And Data fusion is commonly regarded as an efficient method that can improve precision of localization. The paper proposed a novel method based on nonparametric estimation techniques and Radial Basis Function (RBF) Neural Networks to decrease the indeterminacy of Time Difference of Arrival (TDOA) and Received Signal Strength Indicator (RSSI) measurements. The different sources of errors for each measurement types cause that the Probability Density Functions (PDFs) of measurements are not completely dependent. So, theoretically, the fusion of the two kinds of measurements could be effective. Nonparametric estimation techniques are introduced to resolve the problem that measurements do not completely submit to a known PDF. And RBF networks can partly eliminate the influence of environments by regulation of weights. The paper theoretically demonstrated that the data fusion based on RBF networks could achieve location estimation with the Minimum Mean Square Error (MMSE). After that, simulation results of the classical linear combination method and the single RBF fusion were compared with the proposed method in the paper to demonstrate that the proposed method can improve precision of localization with a little of increment in complexion and is robust to the variance of environments.