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System state estimation method based on maximum likelihood criterion robust Kalman filtering

A system state, maximum likelihood technology, applied in the direction of calculation, design optimization/simulation, special data processing applications, etc., can solve the problems of loss of filtering accuracy, inapplicability, etc., and achieve the effect of fast convergence speed and high filtering accuracy

Inactive Publication Date: 2018-09-11
SHANXI UNIV
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Problems solved by technology

In the first paper, when using the Huber robust method, the nonlinear measurement model is linearized and approximated, resulting in a certain loss of filtering accuracy.
Inspired by the idea of ​​iterative Kalman filtering, the second document applies the nonlinear regression idea to the Huber robust processing process, but this method is not suitable for the case of large initial error

Method used

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  • System state estimation method based on maximum likelihood criterion robust Kalman filtering
  • System state estimation method based on maximum likelihood criterion robust Kalman filtering
  • System state estimation method based on maximum likelihood criterion robust Kalman filtering

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Embodiment Construction

[0040] The technical solutions of the present invention will be further described in more detail below in conjunction with specific embodiments. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] A system state estimation method based on the maximum likelihood criterion robust Kalman filter provided by the present invention, the steps of the method include:

[0042] S110: Establish nonlinear system equations of the dynamic system model; wherein, the nonlinear system is expressed as:

[0043] x k =f(x k-1 )+w k-1

[0044] the y k =h(x k )+v k (1)

[0045] Among them: x is the system state variable, f is the nonlinear system function, w is the process noise, y is the measured value, h is th...

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Abstract

The invention discloses a system state estimation method based on maximum likelihood criterion robust Kalman filtering. Under the situations that the system state and a measurement model are both nonlinear, non-Gaussian measurement noise occurs and there are large initial estimation errors, higher convergence speed, higher filtering accuracy and more stable filtering output can be obtained. Through verification and proving of positions, velocity and ballistic coefficient estimation errors, the higher convergence speed and the higher filtering precision are achieved. The method can be applied to spacecraft navigation, target tracking, fault detection, parameter estimation and other fields.

Description

technical field [0001] The invention relates to the field of state estimation, in particular to a system state estimation method based on the maximum likelihood criterion robust Kalman filter. Background technique [0002] In ballistic reentry target tracking applications, the classic algorithm is Extended Kalman Filter (Extended KalmanFilter-EKF). For strong nonlinear measurement models and non-Gaussian measurement errors, in order to improve tracking accuracy and stability, the Maximum Likelihood Criterion-based Robust Kalman Filter (MLCRKF) algorithm can be used. This can not only improve the target tracking accuracy, convergence speed and robustness of the estimation process, but also has important practical significance in the aspects of rapid response and target interception. [0003] After searching the prior art documents, it was found that Karlgaard Christopher D. wrote "Robust Rendezvous Navigation in Elliptical Orbit" [J] / / Journal of Guidance, Control, and Dynami...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 刘美红
Owner SHANXI UNIV
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