Distributed fusion estimation for multi-sensor non-uniform sampling systems with correlated noises and fading measurements

This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor non-uniform sampling systems with correlated noises and fading measurements. The state is updated uniformly and the sensors sample measurement data randomly. The process noise and different measurement noises are correlated at the same instant. Moreover, the fading measurement phenomena may occur in different sensor channels. The independent random variables obeying different certain probability distributions over different known intervals are employed to describe the phenomena. Based on the measurement augmentation method, the state space model is reconstructed in which the asynchronous sampling estimation problem is transformed to the synchronous one. Afterwards, local optimal filters are designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrices between any two local filters are derived. At last, the optimal matrix-weighted distributed fusion filter is given in the linear unbiased minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.