Hierarchical state estimation method based on model prediction extended Kalman filtering

Abstract

The invention provides a hierarchical state estimation method based on model prediction extended Kalman filtering. The method comprises the steps of: firstly, converting a nonlinear coupling system model into a linear classification model and a nonlinear classification model, performing prediction based on the linear classification model and the nonlinear classification model on a state estimationvalue at the previous moment through first-stage extended Kalman filtering, and obtaining a linear state component estimation value; secondly, using the linear state component estimation value for feeding back the nonlinear grading model, and obtaining a nonlinear prediction model; and finally, performing nonlinear prediction model-based prediction on the linear state component estimated value byusing secondary extended Kalman filtering to obtain a nonlinear state component estimated value. According to the hierarchical state estimation method provided by the invention, the system state estimation precision is improved, the calculation dimension of the estimation process is reduced, and the hierarchical state estimation method is suitable for high-dimensional and linear state-decoupled coupled complex system state estimation.

Description

technical field [0001] The invention relates to the technical field of model prediction, in particular to a hierarchical state estimation method based on model prediction extended Kalman filter. Background technique [0002] The application of estimation theory in the field of control, navigation, guidance and target tracking is ubiquitous. However, in estimation theory, the system model is usually regarded as an ideal linear system model or a nonlinear system model. In practical engineering, the system is often a high-dimensional, coupled complex system with both linear and nonlinear states. For the state estimation of high-dimensional and coupled complex systems with linear state decoupling, there are two problems: [0003] (1) For complex coupled nonlinear systems with linear state drives, neither the current linear estimation method nor the nonlinear estimation method can meet the requirements of system estimation accuracy and computational load at the same time. [0...

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Problems solved by technology

[0006] In view of the deficiencies in the above-mentioned background technology, the present invention proposes a hierarchical state estimation method based on model prediction extended Kalman filter, which solves the technical problems of high computational complexity and low precision existing in the existing system state estimation method

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