Sequential Inverse Covariance Intersection 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 scheme 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 new 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, the non-augmented state estimator is proposed by applying an innovation analysis approach. Finally, the sequential inverse covariance intersection fusion estimation algorithm is proposed for the multi-sensor case. The simulation research verifies the effectiveness of the proposed estimation algorithms.