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A Smooth Constrained Extended Kalman Filter Method Applied to Nonlinear Gaussian Models

A technique of extending Kalman and Gaussian models, applied in the field of smooth constrained extended Kalman filtering, to achieve the effect of statistical error convergence

Active Publication Date: 2022-02-08
SUN YAT SEN UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, the above scheme cannot control the gain of the Kalman filter to be bounded, avoiding the possibility of ill-conditioned matrices in the calculation of the innovation covariance during the state update process

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  • A Smooth Constrained Extended Kalman Filter Method Applied to Nonlinear Gaussian Models
  • A Smooth Constrained Extended Kalman Filter Method Applied to Nonlinear Gaussian Models
  • A Smooth Constrained Extended Kalman Filter Method Applied to Nonlinear Gaussian Models

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[0085] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part 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 fall within the protection scope of the present invention.

[0086] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relationship between the components in a certain posture (as shown in the accompanying drawings). Relative positional relationship, movement conditions, etc., if the specific posture changes, the directional indication will also change accordingly.

[0087] In addition, in t...

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Abstract

The invention discloses a smoothing constrained extended Kalman filter method applied to a nonlinear Gaussian model, which establishes a bilateral limiting constraint model for statistical measurement noise of a nonlinear stochastic system, constructs an objective function according to the idea of ​​maximum a posteriori estimation, and uses nonlinear The least squares Newton iterative method approximates the feasible region, selects the feasible region and transmits and updates it, thereby avoiding the irreversible ill-conditioned matrix that may occur in the calculation of the high-order Hessian matrix of the nonlinear system and the calculation of the innovation covariance during the state update process, ensuring the system The Kalman filter gain is bounded and the statistical error converges.

Description

technical field [0001] The invention relates to the technical field of nonlinear filtering, in particular to a smoothing constrained extended Kalman filtering method applied to a nonlinear Gaussian model. Background technique [0002] In practical engineering applications, for nonlinear stochastic dynamic system state estimation, due to the inherent nonlinearity of the observation system and the uncertainty of the external measurement environment, the optimal Kalman filter algorithm (Kalman filter, KF) cannot obtain an analytical solution. The basic idea of ​​the extended Kalman filter (EKF) algorithm is to linearize the nonlinear system function at the estimation point of the Kalman filter. Due to its simple implementation and high computational efficiency, this algorithm is the most widely used nonlinear filtering algorithm in practical engineering. However, due to the existence of linearization errors, when the dynamic system is nonlinear or the measurement noise is larg...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H17/02
CPCH03H17/0257H03H17/0282H03H17/0211
Inventor 张宏伟张小虎杨夏
Owner SUN YAT SEN UNIV