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Self-adaptive high-order volume Kalman filtering method

A Kalman filter and high-order volume technology, applied in the field of signal processing, can solve problems such as filter performance degradation and system state mutation, and achieve the effects of suppressing filter errors and strong tracking capabilities

Inactive Publication Date: 2014-07-16
ZHENGZHOU COLLEGE OF ANIMAL HUSBANDRY ENG
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AI Technical Summary

Problems solved by technology

In addition, in practical applications, there will be sudden changes in the system state, which also greatly reduces the filtering performance.

Method used

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Embodiment Construction

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] refer to figure 1 , let the state-space model of the nonlinear dynamic system be:

[0020] x(k+1)=f(x(k))+w(k)

[0021] z(k)=h(x(k))+v(k)

[0022] where x(k)∈R n Indicates the system state (R n is the complete set of n-dimensional column vectors), z(k)∈R m is the measurement vector, f(x(k)) and h(x(k)) are differentiable functions, w(k)∈R n and v(k)∈R m Both are Gaussian white noise with zero mean, and their variances are Q(k) and R(k) respectively, and the system noise variance Q(k) is time-varying unknown.

[0023] Suppose the initial state of the system is: P(0|0)=p(0), and x(0) is independent of w(k) and v(k), respectively.

[0024] Below, based on the system model, the specific implementation steps of AHCKF are described in detail:

[0025] Step 1 sets the initial conditions for filtering: P(0|0)=p(0),

[0026] Step 2 time up...

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Abstract

The invention relates to a self-adaptive high-order volume Kalman filtering method in the field of signal processing. The self-adaptive high-order volume Kalman filtering method includes: estimating the one-step predicted target state x (k / k-1) and a covariance matrix P (k / k-1) of the same; calculating fading factor h (k) and using the same to adjust the corrected covariance matrix Po (k / k-1); calculating optimal linear estimation x (k / k) and error covariance P (k / k) of the target state, and estimating the variance Q (k) of system noise in real time. The self-adaptive high-order volume Kalman filtering method has the estimation accuracy higher than that of unscented Kalman filter and volume Kalman filter. In addition, through real-time estimation of the variance of the system noise, filter errors caused by unknown time-varying of noise statistic properties are effectively restricted.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to an adaptive high-order volumetric Kalman filtering method. Background technique [0002] In many fields such as signal processing, target tracking and system control, the problem of system state estimation has always been the focus of attention. For linear Gaussian systems, the Kalman filter (KF) method is usually used. However, in many practical application problems, the system state equation or measurement equation often has strong nonlinear characteristics, so the filter estimation problem also shows nonlinearity. The most common method for system estimation in this case is the Extended Kalman Filter (EKF). This method first truncates the first-order linearization of the Taylor expansion of the nonlinear function, and assumes that the state after linearization still obeys the Gaussian distribution, and finally uses KF to obtain the state estimation of t...

Claims

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Application Information

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IPC IPC(8): G06F19/00H03H21/00
Inventor 史岳鹏
Owner ZHENGZHOU COLLEGE OF ANIMAL HUSBANDRY ENG
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