Abstract
In order to improve localization accuracy in mobile sensor network, based on quadratic programming-unscented Kalman filter (QP-UKF) and multidimensional scaling-MAP (MDS-MAP) methods, this paper studies node localization in mobile sensor networks. First, from the perspective of wireless sensor network overall localization, a nonlinear dynamic relative motion model for sensor network units is established. On the basis of that, focused on the physical constraints in the model, the QP-UKF method is introduced to estimate the ranges among nodes in senor network unit. Next, based on the MDS-MAP method and ranges information, a distributed localization algorithm, including clustering, local-localization and merging, is proposed. Finally, a complete range-based localization algorithm for mobile sensor network is designed. The simulation results show that under the same range error ratio, the localization accuracy of the proposed algorithm nearly doubled compared with the existing algorithm.
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