Interactive T-S fuzzy semantic model estimation method and system and computer readable storage medium

A fuzzy semantics and fuzzy model technology, applied in the field of target tracking, can solve the problems of reducing the robustness and accuracy of research results, and the filtering algorithm is difficult to meet the system performance, so as to increase the robustness and accuracy, and enhance the adaptability Effect

Active Publication Date: 2019-04-02
SHENZHEN UNIV
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Problems solved by technology

[0005] The main purpose of the present invention is to provide an interactive T-S fuzzy semantic model estimation method, system and computer-readable storage medium, aiming at so

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  • Interactive T-S fuzzy semantic model estimation method and system and computer readable storage medium
  • Interactive T-S fuzzy semantic model estimation method and system and computer readable storage medium
  • Interactive T-S fuzzy semantic model estimation method and system and computer readable storage medium

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[0020] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

[0021] see figure 1 , is an interactive T-S fuzzy semantic model estimation method, including: S1, defining different semantic fuzzy sets in the T-S fuzzy model according to different language values ​​adopted by the target features; S2, setting each semantic fuzzy set according to different semantic fuzzy sets The probability conversion met...

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Abstract

The invention discloses an interactive T-S fuzzy semantic model estimation method and system and computer readable storage medium, which are used for target tracking and solve the problem that the robustness and the accuracy of a research result are reduced because an existing filtering algorithm is difficult to meet the requirement of system performance, including defining different semantic fuzzy sets in a T-S fuzzy model by adopting different language values; setting a probability conversion method among the semantic fuzzy sets; carrying out fuzzy interaction on the initial state of the target to obtain mixed initial state estimation and mixed initial state covariance; carrying out hybrid initial state estimation and hybrid initial state covariance nonlinear filtering processing to obtain an updated state and an updated state covariance; performing calculation and updating on the precursor parameters of the T-S fuzzy model to obtain updated precursor parameters; calculating a standardized model probability; obtaining state estimation and covariance estimation of the target; and the motion state of the target is estimated, so that the robustness and accuracy of the research result are improved.

Description

technical field [0001] The present invention relates to the technical field of target tracking, in particular to an interactive T-S fuzzy semantic model estimation method, system and computer-readable storage medium. Background technique [0002] Takagi and Sugeno proposed the T-S fuzzy model in 1985, and the then part of the model is expressed in the form of a linear function. [0003] The state estimation of nonlinear and non-Gaussian stochastic systems has a wide range of applications in modern signal processing, image processing, computer vision and automatic control; extended Kalman filter, unscented Kalman filter, volumetric Kalman filter and many Kalman class Improve filtering and other methods. [0004] However, this kind of filtering method can only complete the system filtering of weak nonlinear and noise approximate Gaussian model. When the system is strongly nonlinear and non-Gaussian noise, the filtering algorithm is difficult to meet the requirements of system...

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Application Information

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IPC IPC(8): G06F17/27
CPCG06F40/30
Inventor 李良群谢维信刘宗香
Owner SHENZHEN UNIV
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