Method for overall estimation of adaptive two-stage square root volume filtering

Abstract

The invention discloses a method for overall estimation of adaptive two-stage square root volume filtering. The method comprises the steps of firstly, deducing a two-stage volume Kalman filtering algorithm by use of a square root of an error covariance matrix in place of the square root of the error covariance matrix in participation in a recursive algorithm; then, providing an adaptive two-stagesquare root volume Kalman filtering algorithm based on a Sage-Husa filtering algorithm; and performing overall estimation on unknown noise statistical characteristics in a two-stage volume Kalman filtering process, and performing recursive estimation by taking the estimated noise statistical characteristics as a known condition. Through the overall estimation of the adaptive two-stage square rootvolume Kalman filtering algorithm (ATSCKF-G), measurement for the noise statistical characteristics is directly estimated by use of a Sage-Husa filter, and then the estimated statistical characteristic value is taken as the known condition.

Description

technical field [0001] The invention relates to the field of filter estimation, in particular to a method for global estimation self-adaptive two-stage square root volumetric filtering. Background technique [0002] The nonlinear filtering algorithm is a process of using discrete sensor observations to estimate the continuous state of the target and filtering random noise under the nonlinear system model. At present, several common nonlinear Kalman filters have their own advantages and disadvantages. Extended Kalman filtering (EKF) performs Taylor expansion on nonlinear functions and ignores high-dimensional items for linearization. However, this method is only suitable for weak nonlinear functions whose system models are smooth enough. If the system is a strongly nonlinear system, it will be affected by filtering The error is large and the effectiveness is lost. At the same time, the Jacobian matrix needs to be calculated during the calculation, and the calculation amount ...

AI Technical Summary

Problems solved by technology

In the filtering iteration process, the existence of some factors will cause the error covariance matrix to be negatively definite, such as rounding errors in numerical calculations, large initial value errors, and large observation noises, etc., which will cause the filter to be unstable or even unable to work.

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