Spacecraft attitude determination method based on central error entropy criterion volume Kalman filtering

Abstract

The invention discloses a spacecraft attitude determination method based on central error entropy criterion volume Kalman filtering, and the method comprises the steps: building a nonlinear system according to spacecraft measurement data and an attitude dynamic model; according to the state and the state covariance of the spacecraft at the previous moment, using a Cholesky decomposition method and a preset volume point, solving a volume sampling point, carrying out state transfer, and obtaining a one-step prediction state estimated value and a one-step prediction state covariance of the spacecraft at the current moment are obtained; according to the one-step prediction state estimation value and the one-step prediction state covariance, using a Cholesky decomposition method and a preset volume point, solving a volume sampling point, transmitting measurement information, and obtaining a one-step prediction value, covariance and cross covariance of spacecraft measurement output quantity; and establishing a linearized regression equation of the spacecraft state based on a center error entropy criterion, determining a cost function of center error entropy criterion filtering, and obtaining the current spacecraft state and the state covariance. According to the method, the attitude estimation precision during non-Gaussian noise processing can be improved.

Description

technical field [0001] The invention relates to the technical field of spacecraft attitude estimation, in particular to a method for determining the attitude of a spacecraft based on a central error entropy criterion volumetric Kalman filter. Background technique [0002] High-precision and high-reliability attitude determination is the basis for spacecraft to carry out missions such as space on-orbit services. At present, the attitude determination methods of spacecraft can be divided into two categories: deterministic methods and state estimation methods according to different attitude calculation methods. Uncertainty effects on the reference vector. [0003] In the nonlinear pose estimation process, the Extended Kalman Filter (EKF) algorithm is mainly used for pose estimation. However, due to the limitations of the extended Kalman filter itself, its filtering accuracy is not high under strong nonlinear conditions. In order to overcome the problems of using the extended...

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Problems solved by technology

[0005] However, among the above-mentioned non-Gaussian filters, the particle filter has a large amount of computational complexity, and there are intractable particle degradation and particle impoverishment problems; the Huber volumetric filter based on the Huber cost function has limited accuracy in dealing with non-Gaussian noise; the maximum The correlation entropy volumetric filter also has low accuracy when facing more complex non-Gaussian noise; the minimum error entropy volumetric filter has translation invariance and cannot make the error probability density function tend to zero. At the same time, it is found in practice that The existing minimum error entropy volume filter is prone to filter divergence, and there is a certain degree of instability

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