Distributed Fusion Tracking Estimation Under Range-Only Measurement

This paper is concerned with the distributed fusion estimation problem of range-only target tracking system with unknown but bounded noises, where the linear and nonlinear motion models are both considered. Particularly, a kind of nonlinear transformation is used to convert the nonlinear distance measurement model into a linear one, which eliminates the corresponding linearization errors in the design of estimation error system. In spite of the transformed measurement noise becomes more complicated, while it is still bounded. Moreover, for the nonlinear target motion model, the state linearization error caused by the Taylor expansion is modeled by the state dependent matrix with uncertainty bounded matrix. In this case, based on the bounded recursive optimization algorithm, two kinds of convex optimization problems are established to determine the gains of the local/fusion estimators, and the stability of the designed estimators also can be guaranteed. Finally, two different range-only target tracking systems are presented to show the effectiveness and advantages of the proposed methods.