Noise-related two-stage volume kalman filter estimation method and system

Abstract

The invention discloses a noise-related two-stage volumetric Kalman filter estimation method and system, which adopts the method of identity deformation to transform the noise-related system to establish a system model; by adding coefficients, the system model is transformed from a noise-related system A new system model is established for a noise-uncorrelated system; and then a noise-related two-stage volumetric Kalman filter estimator is obtained by using the noise parameters in the new system model and the recursive calculation of the two-stage filter. A noise-related two-stage volume information filtering algorithm proposed by the present invention uses the product of the cross-covariance and error covariance and the approximate relationship of the Jacobian matrix to eliminate the Jacobian matrix, ensuring the application of the algorithm in high-dimensional nonlinear systems .

Description

technical field [0001] The present invention relates to the field, in particular, to a noise-related two-stage volume Kalman filter estimation method and system. Background technique [0002] The premise of the various two-stage filtering algorithms currently derived is to assume that the nonlinear Gaussian system is noise-independent, that is, the state equation noise and the measurement equation noise are uncorrelated and both are Gaussian white noise, which is the noise situation in an ideal state. . However, in practical applications, the situation related to system noise is ubiquitous. For example, due to the dual influence of the internal components of the system and changes in the external environment, there will be noise correlation. The measurement noise is colored noise. The noise dimension is expanded to After the state, the original system will also be transformed into a noise-related system; in systems that require multi-sensor information fusion, such as maneu...

AI Technical Summary

Problems solved by technology

[0019] To sum up, the existing problems in the existing technology are: the existing technology does not analyze the relevant noise, which reduces the estimation accuracy; the tracking result is poor; the relevant noise is not considered in the estimation process, and the relevant noise is considered as the irrelevant noise. The difficulty of solving is that while estimating the position of the aircraft, the relevant noise cannot be taken into account; there will be filtering divergence, and no further tracking and estimation can be performed at all.

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