Recently

ensemble Kalman filter is considered as an effective solution for the state estimation of nonlinear system.Aiming at the consistency deviation occurred in virtual measurement sampling process on account of measurement noise uncertainty

a novel multi-sensor ensemble Kalman filtering algorithm based on Metropolis-Hastings sampling is proposed.Firstly

combined with the physical properties of multi-sensor measurement system and the generation mechanism of bootstrapping measurement in ensemble Kalman filter

multi-sensor bootstrapping measurement set is structured.Secondly

through solving the likelihood of multi-sensor bootstrapping measurement and designing the probability function of measurement acceptance

validation measurement from multi-sensor bootstrapping measurement set is confirmed by Metropolis-Hastings sampling strategy.The new method corrects the consistency deviation appearing at bootstrapping measurement by means of the extraction and utilization for the redundancy and complementary information in multi-sensor measurement

and improves the filtering precision for the estimated system state.Finally

the theoretical analysis and experimental results show the feasibility and efficiency of our proposed algorithm.