Optimal and self-tuning information fusion filters for systems with unknown stochastic system bias

Based on three fusion estimation algorithms weighted by matrices, diagonal matrices and scalars, distributed information fusion Kalman filters for system state and bias are given for stochastic systems with unknown stochastic system bias, respectively. When the noise statistical information is unknown, a distributed identification algorithm is given by using correlation functions. Further, distributed self-tuning information fusion filters for system state and bias are presented. Simulation example shows the effectiveness of algorithms.