Kalman filter implementation method and device, storage medium and equipment

Abstract

The embodiment of the invention provides a Kalman filter implementation method, a storage medium and an electronic device, and the method comprises the steps: obtaining and initializing a system parameter, obtaining measurement information, calculating an effective Kalman gain according to the measurement information and a posterior observation noise statistic, and obtaining a Kalman filter. Obtaining a measured probability density function according to the linear observation model and the linear property of multivariate Gaussian distribution, calculating the expectation of posterior observation noise distribution of sampling points obtained through importance sampling according to the probability density function, and updating the posterior observation noise statistics, and updating a state covariance matrix according to the effective Kalman gain and the updated posterior observation noise statistics, updating the state according to the updated state covariance matrix, and repeatedly executing the steps from obtaining the measurement information to updating the state covariance matrix until the state is converged. According to the invention, the calculation speed and the estimation precision of the Kalman filter can be improved.

Description

technical field [0001] The present invention relates to the technical field of filtering, in particular to a method, device, storage medium and equipment for realizing a Kalman filter. Background technique [0002] Designing an optimal filter requires accurate knowledge of the statistical model containing the underlying stochastic process, however, in many engineering applications, it is impossible to know the full information of the model due to complexity, practical limitations, limited data, etc. Therefore, it is meaningful to design a robust filter that has good performance against a class of uncertainty models that are compatible with partial prior knowledge. The Kalman filter has been widely used in navigation, target tracking, etc. The structure of the filter is simple and can be realized in the time domain, but the accuracy of noise statistics is very high. [0003] With the in-depth research on Kalman filtering technology, various types of Kalman filtering algorith...

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