High-order extended Kalman filter design method based on maximum correlation entropy

A technique of extended Kalman and maximum correlation entropy, applied in the field of high-order extended Kalman filter design, which can solve problems such as divergence and filter performance degradation

Pending Publication Date: 2021-06-25
HANGZHOU DIANZI UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The present invention can solve the problems of filter performance degradation and divergence in the case of nonlinear non-Gaussian systems, and can be applied to the fields of real-time estimation and target tracking

Method used

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  • High-order extended Kalman filter design method based on maximum correlation entropy
  • High-order extended Kalman filter design method based on maximum correlation entropy
  • High-order extended Kalman filter design method based on maximum correlation entropy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0173] Embodiment 1 considers that the state equation is a high-order polynomial, and the measurement equation is a UAV motion system with a linear model

[0174]

[0175] Among them, w(k+1)~0.8N(0,0.01)+0.2N(0,0.1), and v(k+1)~N(0,0.01), the initial value x(0)=[1 1] T , the initial estimate of Initial estimation error covariance P(0|0)=0.01×diag(1,1).

[0176] Using MCEKF and the proposed H-MCEKF two filtering methods to estimate the state variables in two cases, compare the estimated values ​​of displacement and velocity and the estimation errors of displacement and velocity. The accuracy rates of MCEKF and H-MCEKF were calculated for comparison.

Embodiment 2

[0177] Embodiment 2 The state equation and the measurement equation are all UAV motion systems in the form of high-order polynomials

[0178]

[0179] Among them, w 1 (k)~N(0,0.01),w 2 (k)~N(0,0.01),w 1 (k) and w 2 (k) is an uncorrelated Gaussian white noise sequence, v(k+1)~0.8N(0,0.01)+0.2N(0,0.1), initial value x(0)=[1 1] T , the initial estimate of Initial estimation error covariance P(0|0)=0.01×diag(1,1).

[0180] Using MCEKF and the proposed H-MCEKF two filtering methods to estimate the target state variables in two cases, compare the estimated values ​​of displacement and velocity and the estimation errors of displacement and velocity. The accuracy rates of MCEKF and H-MCEKF were calculated for comparison.

[0181] Embodiment 1 takes case one σ=0.5, ε=10 respectively -1 And case two σ=3,ε=10 -4 As an example, two filtering methods, MCEKF and H-MCEKF, are given to estimate the motion state of the UAV, such as Figure 3 to Figure 8 shown. Table 1 summarize...

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Abstract

The invention discloses a high-order extended Kalman filter design method based on maximum correlation entropy. According to the method, the method comprises the steps: two one-dimensional random variables are given, and the correlation entropy of a random variable pair under limited data driving is obtained; then a state model and a measurement model of the motion of the unmanned aerial vehicle are given; a high-order polynomial in a state model is defined as a hidden variable of a system, the state model of the system is represented in a pseudo-linearization mode, a measurement model is represented in a pseudo-linearization mode in the same way, and linear forms of the state model and the measurement model are obtained; for the state model and the measurement model in the linear form, a high-order extended Kalman filter is obtained by using a recursive filter design thought; and the high-order extended Kalman filter based on the maximum correlation entropy is designed by using the correlation entropy form of the multi-dimensional independent vector and the obtained high-order extended Kalman filter. According to the method, the problems of reduction and divergence of filtering performance under the condition of a nonlinear non-Gaussian system can be solved, and the method can be applied to the fields of real-time estimation and target tracking.

Description

technical field [0001] The invention belongs to the field of filter design, and specifically relates to proposing a novel high-order extended Kalman filter design method by using maximum correlation entropy for a class of nonlinear non-Gaussian systems with polynomial forms. Background technique [0002] The application of filters occupies an important position in various fields at home and abroad, and its progress and development play an important role in national economic construction, especially national defense construction, such as real-time estimation and target tracking. In 1960, for linear systems, Kalman et al. proposed the Kalman filter under the minimum mean square error criterion, and it was widely used rapidly. In order to solve nonlinear problems, based on the Kalman filter, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Volumetric Kalman Filter (CKF) have emerged successively. However, the above filtering requires its modeling error to be...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F17/18G06F17/16G06F17/11
CPCG06F30/20G06F17/18G06F17/16G06F17/11
Inventor 崔体坡孙晓辉文成林袁洢苒李建宁
Owner HANGZHOU DIANZI UNIV
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