Research On Fusion Estimation Of Nonlinear Multi-sensor Systems With Correlated Noise And Missing Observations

With the continuous development of science and technology,the traditional singlesensor linear filtering algorithm no longer meets the requirements.People pay more attention to the application of multi-sensor nonlinear systems based on reality.Practical systems often exhibit phenomena such as non-linearity,data packet loss and disorder,noise correlation,and slow system operation due to excessive sensors.These phenomena will lead to large estimation errors in the process of filter settlement of multi-sensor nonlinear systems,and even problems such as filter divergence.Therefore,the problem of fusion estimation of nonlinear multi-sensor systems with correlated noise and missing observations has become one of the hot research issues in the field of estimation.This article first describes three classic nonlinear filtering algorithms and gives a simulation analysis.Then,we use the decorrelation method and the measurement information expansion method to deal with the correlation between the system noise and the observation noise,and the one-step correlation between the observation noise itself.Next,for the problem of data packet loss and the disorder of measurement data,the mean value method and the nearest neighbor value method are proposed.Finally,combined with Taylor series,a weighted observation fusion algorithm for nonlinear systems is proposed.In this paper,the algorithm is applied to the ultra-wideband indoor positioning system,and the feasibility and effectiveness of the algorithm are verified through practical application.