Fuzzy adaptive variational Bayesian unscented Kalman filter method

A variational Bayesian and unscented Kalman technique, applied in the field of signal processing, can solve problems such as failure and uncertainty filtering methods

Inactive Publication Date: 2014-10-22
LUOYANG INST OF SCI & TECH
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Problems solved by technology

For practical application systems, the variance of measurement noise is always time-varying and unknown, because the measurement system is disturbed by various internal and external fa...

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  • Fuzzy adaptive variational Bayesian unscented Kalman filter method
  • Fuzzy adaptive variational Bayesian unscented Kalman filter method

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Embodiment Construction

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] refer to figure 1 , the state space model of the nonlinear dynamic system is set as:

[0034] (1)

[0035] in, Indicates the system state ( is the complete set of n-dimensional column vectors), is the measurement vector, and are differentiable functions. and are Gaussian white noise with zero mean, and their variances are and , and the measurement noise variance is time-varying unknown.

[0036] Suppose the initial state of the system is: , ,and respectively independent of and .

[0037] Below, based on the system model, the specific implementation steps of FAVB-CKF are described in detail:

[0038] step 1 Set the filtering initial conditions, including:

[0039] (1.1) Initial state and its covariance matrix ;

[0040] (1.2) The moving window size W in the fuzzy logic met...

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Abstract

The invention provides a fuzzy adaptive variational Bayesian unscented Kalman filter method. The method comprises the steps of estimating a one-step prediction target state as shown in the description and a covariance matrix thereof as shown in the description, iteratively estimating the variance as shown in the description of the measured noise, calculating the true value as shown in the description, the estimate value as shown in the description, the matching degree index as shown in the description and the adjustment quantity as shown in the description of a residual variance matrix at the current moment, and the adjusted measured noise variance as shown in the description, and calculating the estimated value as shown in the description of the target state and the error covariance matrix thereof. The method is capable of estimating the statistic variance capacity of the measured noise on line, and therefore, the filter error caused by unknown time variant of the noise statistical property is reduced and nonlinear filter estimation accuracy is improved. Meanwhile, the fuzzy logic method based on the innovated covariance matching technique is used for adjusting the measured noise variance estimated by the variational Bayesian method in real time, inhibiting the divergence of the filter and enhancing the robustness of the filter method.

Description

technical field [0001] The invention relates to a method in the technical field of signal processing, in particular to a fuzzy adaptive variational Bayesian unscented Kalman filtering method. Background technique [0002] Nonlinear stochastic dynamic systems are widely encountered in practical applications, such as rocket guidance and control systems, aircraft and ship inertial navigation systems, satellite orbit / attitude estimation, integrated navigation, radar or sonar detection And so on belong to this type of system. Even for linear systems, nonlinear filtering problems arise when states and parameters need to be estimated simultaneously. Moreover, nonlinear filtering problems widely exist in many scientific fields, so the state estimation of nonlinear systems is very important both in theory and in engineering. [0003] The most commonly used nonlinear system filtering method is extended Kalman filter (extended Kalman filter, EKF). EKF linearizes the nonlinear model ...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 王国勇王剑李冠峰李明照崔文孙昭峰王帆张红霞
Owner LUOYANG INST OF SCI & TECH
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