Localization for mobile sensor network based on unscented Kalman filter with clipping and uncorrelated conversion

Abstract

This article studies localization for mobile sensor network with incomplete range measurements, that is, the ranges between some pairs of sensors cannot be measured. Different from existing works that localize sensors one by one, the localization problem in this article is solved for the whole mobile sensor network. According to whether the ranges can be measured or not, all the sensors in the network are grouped to construct basic localization units. For the sensors in basic localization units, a constrained nonlinear model is first established to formulate their relative motion, where the motion states are chosen as ranges and cosine values of angles between ranges. Then, based on the established model, a constrained unscented Kalman filter is adopted to provide motion state estimation. In the constrained unscented Kalman filter, the clipping technique is introduced to handle the model constraints, and the uncorrelated conversion technique is introduced to make full use of measurements. Hence, the estimation accuracy can be improved. Finally, the distributed multidimensional scaling-map method is used to localize the whole sensor network using the estimated ranges, and a localization algorithm is presented. The effectiveness and advantages of the proposed algorithm are demonstrated through several simulation examples.