Distributed Fusion Estimation for Multi-rate Multi-sensor Time-delayed Systems with Fading Measurements

This paper proposes a distributed fusion estimator for asynchronous multi-rate multi-sensor systems with time delays and fading measurements. The state is updated uniformly and different sensors uniformly sample measurements with different sampling rates, time delays and fading measurement rates. The independent random variables obeying certain probability distributions over different known intervals are employed to describe the fading measurement phenomena. First, a new state space model at measurement sampling points is constructed where the time-delayed system is transformed to a delay-free one with correlated noises in limited time intervals. Then, the real-time optimal local filters at measurement sampling points and at state update points are derived based on the new state space model. Further, estimation error cross-covariance matrices between any two local estimators are derived. Finally, a real-time distributed fusion estimator at state update points is obtained based on the optimal fusion criterion weighed by matrices in the linear unbiased minimum variance sense. A numerical example is given to illustrate the effectiveness of the proposed algorithms.