A State Estimation Method for Multisensor Uncertain Systems Based on Sequential Fusion and Zonotope

Abstract

Most actual multi-sensor systems are usually affected by unexpected distractions such that the probability distributions of noises may become unforeseeable. For the classical state estimation methods, if the probability distributions of noises are not accurate, it will cause large estimation errors or even filtering performance divergence. Therefore, a fast and accurate measurement-sequence-fusion-zonotope (MSF-Z) state estimation method is proposed, which is the first attempt for multi-sensor sequential fusion to use only the boundary knowledge without using probability distributions of noises. The method firstly gives a family of zonotopes containing the intersection of the predicted state set and the measurement polytope, where all measurements are fused one by one according to their time sequence arriving at the fusion center, which improves the real-time performance, meanwhile, the dimension of the output matrix is reduced, which reduces the computational complexity. Then, find a zonotope with the smallest volume as the external description of the intersection. Next, obtain the compact set of the estimated state using the reduction operator to improve accuracy, which effectively solves the problem of multi-sensor state estimation when the probability distributions of noises are unknown. Finally, the simulation of a turbofan engine control system verifies that the proposed method has high accuracy and good time performance.