Multiple-model self-calibration Kalman filter (MSKF) method

Abstract

The invention provides a multiple-model self-calibration Kalman filter (MSKF) method. The method comprises the following steps: 1, establishing system basis equations; 2, carrying out filter initialization on a system consisting of the formula (1), the formula (2) and the formula (3); 3, carrying out time updating on the system; 4, carrying out iterative variable updating; 5, carrying out measurement updating; and 6, carrying out iterative calculation. According to the method, a multiple-model estimation theory is introduced into the problem that system state equations are under unknown inputinterference, and a complete process of the multiple-model self-calibration Kalman filter method is obtained on the basis of a self-calibration Kalman filter and a standard Kalman filter; the problemthat the standard Kalman filter is divergent in filtering in a non-zero segment of unknown input is solved, and filtering precision of the self-calibration Kalman filter in a segment that unknown input is zero is also significantly improved; and filtering precision of the segments that unknown input is or is not zero is improved at the same time, an applicable range is further expanded, and systemrobustness is also further improved on the basis of a self-calibration Kalman filter method (SKF).

Description

【Technical field】 [0001] The invention provides a multi-model self-calibration Kalman filtering method, which belongs to the technical field of robust Kalman filtering. 【Background technique】 [0002] Kalman filtering is a method of estimating the system state by using the system state equation and measurement equation. Since it was proposed in 1960, it has been widely used in signal processing, navigation, deep space exploration and fault diagnosis and other fields. The standard Kalman filter method is only for linear systems. On this basis, researchers have proposed Extended Kalman Filter (Extended Kalman Filter, EKF), Unscented Kalman Filter (Unscented Kalman Filter, UKF) and Particle Filter (Particle Filter). ,PF) and a series of methods, they extend the scope of application of the Kalman filtering method to nonlinear systems, and further improve the filtering accuracy. [0003] However, whether the above filtering methods are aimed at linear systems or nonlinear system...

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Problems solved by technology

[0004] But due to the uncertainty of the system, the unknown input of the system state equation may also be zero

In this case, since the self-calibration Kalman filter method introduces an unknown input estimation term, although this term will gradually converge to zero as the filtering progresses, considering the volatility, time delay and error of the convergence process , its filtering accuracy is not as good as the standard Kalman filtering method

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