A Distributed Positioning Scheme Based on Neural Network for Radiation Source

Abstract

To avoid the high calculating requirement of the central node in the centralized positioning system, a distributed scheme with multiple localization sub-networks to localize an unknown radiation source (RS) is discussed in this paper. Furthermore, in order to reduce the adverse effect of the potential information loss in the parameter estimation of the traditional two-step localization algorithm, a neural network to deduce the objective position from the hybrid localization parameters is designed for each sub-network to replace the solving of positioning equations involved in the two-step localizer. Finally, in order to obtain more accurate localization results, two fusion methods based on the weighted sum and the neural network are given at the final control center to fuse the localization results of all the sub-networks. The experimental results show that the proposed scheme significantly outperforms the traditional single-parameter based two-step localization method even though the latter one adopts the centralized positioning strategy. Furthermore, the neural network based fusion method can improve the final positioning precision obviously for signal to noise ratio (SNR) higher than 10dB.