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State estimation method for high-accuracy nonlinear system

A technology of nonlinear systems and nonlinear states, applied in computing, special data processing applications, instruments, etc., can solve the problems of necessity, urgency, and unsatisfaction

Inactive Publication Date: 2014-08-13
CIVIL AVIATION UNIV OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Although the estimation accuracy of SUKF has been improved compared with EKF, it still cannot meet the requirements of the nonlinear system state estimation problem with high precision requirements, and there is still necessity and urgency to further improve the state estimation accuracy

Method used

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  • State estimation method for high-accuracy nonlinear system
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  • State estimation method for high-accuracy nonlinear system

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Embodiment 1

[0123] figure 2 The state x in the system shown in the expression (5) k A representative estimate of where h(x k+1 )=△tx k+1 (1-0.5x k+1 ); k ~N(0,b 2 Δt); n k+1 ~N(0,d 2 Δt). Δt represents the sampling interval time. There are three equilibrium points in this system, which are at 0 and ±1 respectively, where the system is unstable at 0 and stable at ±1.

[0124] In the figure, SUQKF represents the method of the present invention, which is named as: Simplex Unscented Quadrature Kalman Filter (SUQKF) method. SUQKF* represents the state estimation method corresponding to different order sampling points, such as SUQKF2 represents the state estimation method corresponding to the second order sampling point. It can be seen from the figure that although the estimation of EKF can also tend to a stable state, it is completely opposite to the actual state of the system, that is, the state estimation fails. However, the estimation of SUQKF is basically consis...

Embodiment 2

[0127] Ballistic targets are extremely destructive and dangerous, and the accuracy of state estimation directly affects whether they can be destroyed before landing, so it is of great significance to track them with high precision. The ballistic target reentry tracking system is established as follows:

[0128] x k + 1 = f ( x k ) = x k + Δtg ( x k ...

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Abstract

The invention discloses a state estimation method for a high-accuracy nonlinear system. The method comprises the following steps in sequence: configuring a high-order sampling point which is consistent with nonlinear state transfer function probability distribution; calculating a state vector estimate; calculating a state vector error covariance estimate; configuring a high-order sampling point which is consistent with nonlinear state transfer observation function probability distribution; calculating the estimate of measurement; calculating a filtering gain; calculating a posteriori estimate of a state vector; calculating a state vector error covariance estimate. The method has the advantages that 1, the state estimation accuracy is high; 2, the converging speed is high; 3, resolution of a Jacobian matrix is avoided; 4, redundant weight coefficient debugging distribution is not needed; 5, the problem of dimension disaster is solved.

Description

technical field [0001] The invention belongs to the technical field of nonlinear system state estimation, in particular to a high-precision nonlinear system state estimation method. Background technique [0002] The problem of nonlinear system state estimation is ubiquitous in real life, involving many high-tech fields, such as aerospace, aviation, navigation, automobile technology, etc., and it has been widely concerned by scientific and technological workers from beginning to end. [0003] Extended Kalman Filter (Extended Kalman Filter, EKF), as a nonlinear system state estimation method, has been widely used in practice. It performs state estimation by transforming the nonlinear problem into a linear problem, but the resulting linearization error is large, which easily leads to divergence; in addition, it also needs to solve the cumbersome Jacobian matrix, which makes the calculation process more complicated, especially the state vector This problem will be more prominen...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 韩萍桑威林
Owner CIVIL AVIATION UNIV OF CHINA
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