Abstract:A multisensor distributed extended Kalman filtering algorithm is presented for nonlinear systems, in which the dynamic equations of the systems and the equations of sensor s measurements are linearized in the global estimates and global predictions respectively, and the suboptimal global estimates based on all available information can be reconstructed from the estimates computed by local sensors based solely on their own local information and transmitted to the data fusion center. An analysis of the properties of the algorithm presented here shows that the global estimate has higher precision than the local one and smaller linearization error than the existing method. Finally, an application of the algorithm to radar/IR, tracking of a maneuvering target is illustrated. Simulation results show the effectiveness of the algorithm.