distributed estimation fusion with global track feedback using a modified LOLIMOT algorithm

In this paper, first an enhanced NeuroFuzzy method for modeling nonlinear system is presented. In this method we use EM algorithm for identification of local models, which gain us model mismatch covariance. The achieved model can be stated in state space model as a linear time-varying system. As the noise and model mismatch covariace is known, Kalman filter can be easily used for estimation fusion. Based on available information two commonly used state estimators, based on FLL models are presented, with implementation on an interior permanent magnet synchronous motor (IPMSM) and also kinematic model of a rotating rigid body to validate the proposed method. The simulations show that using data fusion will enhance the estimation accuracy to a great deal also accuracy of centralized estimation fusion is better than distributed estimation fusion.