Decentralized estimation of nonlinear target tracking based on nonlinear filter

The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filter and UKF are both divergent through simulation. Then decentralized particle filter method was used by dividing state and state function into two parts accordingly, through decoupling correlated noise of state function, designing importance function, the two parts were estimated by particle filter separately. The outcome illustrated indicates it not only free of degeneracy, but also having a high accuracy of filtering, shortening time of filtering. At last, a decentralized estimation based on mixed nonlinear filter is addressed, which the weight of local level is not estimated.