Suboptimal distributed Kalman filtering fusion with feedback

In order to improve the accuracy of fusion algorithm, feedback is introduced into Kalman filtering fusion. Fusion center broadcasts its latest estimated states to the local sensors, which can improve the performance of local tracking error through reducing the covariance of each local error, and only needs calculating the trace of error variance matrices without calculating the inverse of error variance matrices. Simulation results show that it can reduce the computational complexity and the covariance of error, and it is convenient for engineering applications.