Abstract: In most applications of distributed multisensor system,the system dynamics or observation equations are nonlinear.The Extended Kalman Filter (EKF) is the most common approach for distributed multisensor nonlinear state estimation.It is well known that the performance of the EKF may not be always good due to the linearization error.In addition,the derivation of the Jacobian matrices is nontrivial in most applications and often lead to implementation difficulties.To address distributed multisensor nonlinear state estimation problems,the paper proposes a novel state estimation method based on the unscented kalman filter.The Unscented Kalman Filter (UKF) is a novel method for nonlinear filtering.The UKF can be more easily applied for nonlinear state estimation,because the UKF does not approximate the nonlinear state and measurement equations.Moreover,The UKF can get more accurate estimation than the EKF.At last,a Monte Carlo simulation is used to analyze the performance of the method.The results of the simulation prove that the new method will get more accurate estimation than the distributed Extended Kalman Filter.

Key words: distributed, nonlinear, multi-sensor, state estimation, unscented kalman filter