Adaptive Estimation Method of Observational Model Systematic Errors for Intelligent Vehicle SINS/GNSS Dynamic Integrated Navigation
To meet the higher demand for navigation and positioning accuracy in the current development of vehicle intelligence and solve the problem of reduced accuracy caused by observational model bias in the nonlinear dynamic integrated navigation and positioning of intelligent vehicles,a novel adaptive estimation method of observational model systematic errors for nonlinear dynamic integrated navigation systems was proposed based on the principle of adaptive filtering.This method can be used to suppress the effects of observational model systematic errors on filtering estimation accuracy and to overcome the defects and deficiencies of existing error estimation methods,thereby improving the positioning accuracy of vehicle dynamic integrated navigation.First,an adaptive unscented Kalman filtering(AUKF)algorithm was developed by constructing an adaptive factor using innovation information and the covariance matrices of predicted observations.Then,the effects of abnormal observations on the dynamic navigation system were suppressed by adaptively adjusting the weight of the state prediction and observational information.Based on the effects of observational model systematic errors,the nonlinear navigation system model was improved and the proposed AUKF was extended to estimate and compensate for observational model systematic errors and thereby improve the accuracy of system state estimation.Finally,the proposed adaptive estimation method was applied to a road test of a vehicle equipped with a strap-down inertial navigation system(SINS)/global positioning system(GPS)nonlinear integrated positioning system on a mountain road,and its performance was verified in two respects:① when considering and not considering the effects of observational model systematic errors,and ② when compared with other filtering methods.Experimental results demonstrate that when compared with existing methods,the proposed method not only can control the effects of abnormal observations on nonlinear navigation systems,it can also effectively estimate and compensate for observational model systematic errors.Accordingly,this method has good filtering accuracy,showing a positioning accuracy of less than 0.46 m.Even in the case of systematic error interference,the positioning accuracy still reaches 0.50 m.The proposed method is superior to other filtering methods in suppressing the interference of observational model systematic errors.The algorithm is simple and easy to implement,achieving a better balance between accuracy and real-time performance.
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