Distributed CI Fusion Estimation for Non-uniform Sampling Systems with Fading Measurements

This paper is concerned with the state estimation problem for non-uniform sampling systems subject to fading measurements. The concerned non-uniform sampling system is that the state is updated uniformly and the measurements are sampled randomly. Moreover, the fading measurement phenomena may occur in different sensor measurement channels where the independent random variables obeying different certain probability distributions over different known intervals are employed to describe this phenomena. Firstly, a state space model is established to depict the dynamics at the measurement sampling points within a state update period. Then, based on single sensor measurements, a non-augmented state estimator is proposed by applying an innovation analysis approach. Finally, a distributed suboptimal fusion estimator is proposed for the multi-sensor case based on the covariance intersection fusion algorithm. It has good robustness and reduced computational cost since it has a parallel structure and avoids calculation of cross-covariance matrices between any two local estimators. The simulation research verifies the effectiveness of the proposed estimation algorithms.