Optimal Sequential Fusion Estimation With Stochastic Parameter Perturbations, Fading Measurements, and Correlated Noises - 2018


This Project focuses on the linear optimal recursive sequential fusion filter design for multisensor systems subject to stochastic parameter perturbations, fading measurements, and correlated noises. The stochastic parameter perturbations existing in the state model are described by white multiplicative noises. The fading measurement phenomena for different sensors are described by freelance random variables with known statistical properties. Moreover, the measurement noises of different sensors are correlated with each other and additionally correlated with the system noise at the identical time step. Initial, a model admire the original system is established by transferring the multiplicative noises into the additive noises. Then, primarily based on the equivalent model and an innovation analysis methodology, a sequential fusion filter in the linear minimum variance sense is proposed to resolve the linear optimal state estimation problem in real time per the coming order of measurements from totally different sensors. Finally, the equivalence on estimation accuracy of the proposed sequential fusion filter and the centralized fusion filter is strictly proven, which shows the optimality of the proposed sequential fusion algorithm. Moreover, the proposed sequential fusion filter contains a reduced computational burden. Compared with the distributed matrix-weighted fusion filter, the computation of cross-covariance matrices is avoided and also the estimation accuracy is improved. Finally, a simulation example verifies the effectiveness of the proposed sequential fusion filtering algorithm.

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