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

Abstract

The invention discloses a spacecraft attitude determination method based on central error entropy criterion unscented Kalman filtering, and the method comprises the steps: building a nonlinear system for spacecraft attitude determination according to spacecraft measurement data and a spacecraft attitude dynamic model; according to the state and the state covariance of the spacecraft at the previous moment, generating a plurality of Sigma points by using a preset sampling mode, establishing a time updating transfer formula, and obtaining a one-step prediction state estimated value and a one-step prediction state covariance of the spacecraft at the current moment; according to the one-step prediction state estimation value and the one-step prediction state covariance, generating a plurality of Sigma points by using a preset sampling mode, and obtaining a one-step prediction value, an auto-covariance and a cross-covariance of the measurement output quantity of the spacecraft; 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 state and the state covariance of the spacecraft at the current moment. According to the method, the attitude estimation precision and robustness 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 spacecraft attitude determination method based on center error entropy criterion unscented 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 existing in the extended Kalman filt...

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Problems solved by technology

[0005] However, among the non-Gaussian filters mentioned above, the particle filter has high computational complexity, and its particle degeneration and particle impoverishment problems are also difficult to deal with; Gaussian and filter assume that the probability density of noise is known , and has a large amount of calculation; the accuracy of the Huber unscented filter is limited; the Student's t filter can only process specific non-Gaussian noise, and the environmental adaptability is poor; although the maximum correlation entropy unscented filter can combine the probability The peak of the density function is fixed to zero, but the accuracy of the algorithm is limited when used alone; the minimum error entropy unscented filter has translation invariance and cannot guarantee that the estimation error converges to zero

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