Research On System Error Of Measurement And State Joint Estimation Method Based On Bayesian Filtering

The main task of the target tracking is to predict and filter the current state of the moving target by the measuring information of the sensor and the priori modeling information of the system state evolution.In some complex systems,the measurement error is difficult to avoid,because of the sensor itself and the external environment.The error characteristics can be divided into random error and system error.Because the system error has a larger order of magnitude than the random error,it is the main factor affecting the tracking accuracy.To this end,this paper focuses on the following two types of environmental background and puts forward the corresponding system error registration method.Aiming at the problem of system error registration in nonlinear systems and under the background of single sensor measurement,this paper presents a system error and state joint estimation algorithm based on Rao-Blackwellized extended Kalman filter.Firstly,the state model of the target parameters and the system error is established by the state expansion method.Then,Rao-Blackwellized modeling is used to split the linear and nonlinear parts.Furthermore,the Kalman filter and the extended Kalman filter are used to estimate the state parameter and the system error.Compared with the traditional extended Kalman filter,the algorithm reduces the state dimension,and reduces the computational complexity and the filter divergence.The experimental results show that the state estimation root mean square error(RMSE)of Rao-Blackwellized extended Kalman filter in both horizontal and vertical directions is significantly lower than that of the traditional extended expansion extended Kalman filter.Aiming at the problem of system error registration in linear system and under the background of multi-sensor measurement,a systematic error and state joint estimation algorithm based on Kalman Consensus Filter is proposed,in this paper.Firstly,the target motion state and the system error registration model are established.Furthermore,the multi-sensor information interaction characteristic of Kalman Consensus Filter is used to optimize the model structure.Finally,the joint parameter estimation and system error registration are realized by the state vector augmentation method.This algorithm extends the Kalman Consensus Filter algorithm to the multi-sensor linear system error registration field,and improves the accuracy of the target tracking while completing the error registration.The convergence speed is fast and the robustness is strong.The experimental results show that compared with the traditional Kalman Consensus Filter,the RMSE of the Kalman Consensus Filter method after registration in the two directions is reduced respectively.The simulation results verify the validity and superiority of the algorithm.