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A filtering method based on an event trigger mechanism

An event-triggered and mechanism-based technology, applied to electrical components, adaptive networks, impedance networks, etc., can solve the problem of large estimation errors in filtering methods, and achieve the effects of reducing estimation errors, easy solution and implementation, and good estimation effects

Active Publication Date: 2019-05-07
HARBIN UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention solves the problem of large estimation errors in existing filtering methods, and proposes a filtering method based on an event trigger mechanism

Method used

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  • A filtering method based on an event trigger mechanism
  • A filtering method based on an event trigger mechanism
  • A filtering method based on an event trigger mechanism

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Experimental program
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specific Embodiment approach 1

[0022] Specific implementation mode 1. Combination Figure 1 Describe this implementation mode, this implementation mode is a filtering method based on an event trigger mechanism, such as figure 1 As shown, the specific steps of the method are:

[0023] Step 1, establishing a dynamic model of a nonlinear stochastic system with multiple measurement loss phenomena with uncertain loss probability;

[0024] Step 2, under the event trigger mechanism, filter design is performed on the dynamic model of the nonlinear stochastic system with multiple measurement loss phenomenon with uncertain loss probability;

[0025] Step 3. Calculate the upper bound Ω of the one-step forecast error covariance matrix of the filter k+1|k ;

[0026] Step 4. According to the upper bound of the one-step forecast error covariance matrix Ω k+1|k , to calculate the filter gain matrix K k+1 ;

[0027] Step five, the filter gain matrix K calculated in step four k+1 Introduce the state estimation formula...

specific Embodiment approach 2

[0031] Specific embodiment two: the difference between this embodiment and specific embodiment one is that in said step one, a dynamic model of a nonlinear stochastic system with multiple measurement loss phenomena with uncertain loss probability is established; the specific process is:

[0032] The state-space form of the dynamic model for nonlinear stochastic systems with multiple measurement dropout phenomena with uncertain dropout probabilities is:

[0033]

[0034] the y k = Ξ k C k x k +h(x k ,ζ k )+ν k (2)

[0035] In the formula, are the state variables at time k and k+1 respectively; the initial value x 0 The mean is Variance is P 0|0 ; is the real field of the state of the dynamic model of the nonlinear stochastic system, n is the dimension; is the measurement output at the kth moment, is the real field of the state of the dynamic model of the nonlinear stochastic system, m is the dimension; η k with ζ k is Gaussian white noise with zero mean ...

specific Embodiment approach 3

[0037] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that the data loss matrix Ξ k =diag{ξ 1,k ,ξ 2,k ,...,ξ m,k}, diag{} is a diagonal matrix; ξ i,k For m variables that are independent of each other with respect to i and k, i=1,2,...,m, obey the Bernoulli distribution with a value of 1 or 0, and satisfy the following conditions:

[0038]

[0039] Where i is the location of data loss, k is the time, is the determined mathematical expectation, Δξ i Describe the uncertainty of probability, i=1,2,...,m, Prob{} is the probability, is the expectation of {};

[0040] The non-linear function g(x k , η k ) and h(x k ,ζ k ) satisfy g(0,η k )=0, h(0,ζ k )=0 and the following conditions:

[0041]

[0042]

[0043]

[0044] Among them, s>0 is a known constant, with Both are nonlinear parameter matrices, r=1,2,...,s; for The transpose of g(x j , η j ), h(x j ,ζ j ) is a nonlinear function, j ...

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Abstract

The invention discloses a filtering method based on an event trigger mechanism, and relates to a filtering method based on the event trigger mechanism. The problem that an existing filtering method islarge in estimation error is solved. The method comprises the following steps: 1, establishing a dynamic model of a nonlinear random system; 2, carrying out filter design on the dynamic model of thenonlinear random system under an event trigger mechanism; 3, calculating the upper bound of the one-step prediction error covariance matrix of the filter; 4, calculating a filtering gain matrix; 5, substituting the filtering gain matrix into the second filtering gain matrix to obtain state estimation at the k + 1 moment; Judging whether k + 1 reaches the total network duration M or not, if k + 1 is smaller than M, executing step 6, and if k + 1 = M, ending; 6, calculating the upper bound of the filtering error covariance matrix; And k is equal to k + 1, and the second step is executed until k+ 1 = M is met. The method is applied to the field of filtering of event triggering mechanisms.

Description

technical field [0001] The invention relates to a filtering method based on an event trigger mechanism. Background technique [0002] The filtering problem of nonlinear stochastic systems is an important research part in networked control systems, and it has been widely used in signal estimation tasks in the fields of system engineering, global positioning systems, and target tracking systems. [0003] Nonlinearity and randomness are ubiquitous in real life, and the existing filtering methods cannot simultaneously deal with nonlinear stochastic systems with multiple measurement loss phenomena with uncertain loss probability, which usually deteriorates the estimation performance of the filter. , the uncertain loss probability will cause the distortion behavior of the signal; [0004] In summary, the existing filtering methods have a large estimation error. Contents of the invention [0005] The invention solves the problem of large estimation error in the existing filteri...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H21/00
Inventor 胡军张红旭武志辉陈东彦石宇静关馨郁徐沈阳张昌露
Owner HARBIN UNIV OF SCI & TECH
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