Self-adaptive UKF (Unscented Kalman Filter) algorithm

Abstract

The invention discloses a self-adaptive UKF (Unscented Kalman Filter) algorithm. In the algorithm, a Kalman filtering algorithm based on a maximum likelihood criterion and the self-adaptive UKF algorithm based on maximum posteriori estimation are combined. Real-time estimation is performed on covariance through the two algorithms, and averaging is performed to obtain an estimated value with a better prior covariance true value tracking effect, so that the filtering accuracy is increased, and the filtering stability is enhanced.

Description

technical field [0001] The invention relates to an adaptive UKF filtering algorithm, in particular to an adaptive UKF filtering algorithm combining maximum likelihood estimation and maximum a posteriori estimation. Background technique [0002] In order to solve the problem that the Kalman filter and the subsequent extended Kalman filter have large errors in estimation and may even cause filter divergence in nonlinear conditions, in recent years, Julier and Uhlman proposed the Unscented Kalman Filter based on the idea of ​​representative points of multivariate functions. (UKF) method, the accuracy of the UKF algorithm is significantly higher than that of the EKF algorithm and the amount of calculation is significantly smaller than that of the EKF algorithm. However, there is a main problem with the UKF algorithm that when the statistical characteristics of the noise are unknown, the accuracy of the UKF filter decreases or even diverges. To solve this problem, various countrie...

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Problems solved by technology

[0002] In order to solve the problem that the Kalman filter and the subsequent extended Kalman filter have large errors in estimation and may even cause filter divergence in nonlinear conditions, in recent years, Julier and Uhlman proposed the Unscented Kalman Filter based on the idea of ​​representative points of multivariate functions. (UKF) method, the accuracy of the UKF algorithm is significantly higher than that of the EKF algorithm and the amount of calculation is significantly smaller than that of the EKF algorithm. However, there is a main problem with the UKF algorithm that when the statistical characteristics of the noise are unknown, the accuracy of the UKF filter decreases or even diverges. To solve this problem, various countries Scholars have proposed many solutions. Among them, literature [7] proposed an algorithm based on the principle of maximum a posteriori estimation and exponential weighting to derive a suboptimal unbiased MAP time-varying noise statistical estimator with fading factors. , the algorithm has the characteristics of self-adaptive ability, simple recursive formula, and easy engineering implementation, but the algorithm needs enough data to achieve a more ideal effect

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