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High-order extended Kalman filter design method of strong nonlinear dynamic system

A technique for nonlinear dynamic and filter design, used in computer-aided design, instrumentation, computing, etc.

Inactive Publication Date: 2021-05-07
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, compared with the extended Kalman filter established based on the idea of ​​partial expansion of Taylor nets, it brings new problems and challenges to the design of high-order extended Kalman filter.

Method used

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  • High-order extended Kalman filter design method of strong nonlinear dynamic system
  • High-order extended Kalman filter design method of strong nonlinear dynamic system
  • High-order extended Kalman filter design method of strong nonlinear dynamic system

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Embodiment Construction

[0064] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0065] The application principle of the present invention is further described below in conjunction with the target position tracking with strong nonlinear motion state on the plane, as figure 1 Shown, the concrete steps of the present invention are:

[0066] The motion equation of the target in step (1) can be written in the following form:

[0067]

[0068]

[0069] Among them, process noise and measurement noise have the following characteristics w 1 (k)~N(0,0.01), w 2 (k)~N(0,0.01), v(k+1)~N(0,0.01), x 1 (k) is the x-axis position of the target, x 2 (k) is the y-axis position of the target.

[0070] Step...

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Abstract

The invention discloses a high-order extended Kalman filter design method of a strong nonlinear dynamic system. The method includes, firstly, modeling a state model and a measurement model of a strong nonlinear dynamic system based on a multi-dimensional high-order polynomial; secondly, defining a high-order polynomial in the state model as a hidden variable of the system, and equivalently rewriting the state model into a pseudo-linear model based on combination of an original variable and the hidden variable; thirdly, taking the high-order hidden variables as additive parameters of each order of the system, and establishing a dimension expansion linear state model combining the state and the parameters by performing random dynamic modeling on the high-order hidden variables; then, processing the measurement model correspondingly, and modeling an original system into a dimension expansion linear measurement model based on combination of states and parameters; and finally, designing a novel high-order extended Kalman filter aiming at original state estimation based on a dimension-extended linear system. According to the invention, the validity of the new filter is verified through digital simulation.

Description

technical field [0001] The invention proposes a novel high-order extended Kalman filter (High-order Extended Kalman Filter, HEKF) design method for a class of strongly nonlinear stochastic dynamic systems with polynomial forms. Background technique [0002] The application of filters plays an important role in various fields at home and abroad. The development and progress of filters have played an important role in national economic construction, especially in world defense construction, such as system status and important Online real-time estimation of parameters, defense, tracking and precision strikes on non-cooperative targets in the military field, etc. Therefore, practitioners put forward a landmark Wiener filter based on the minimum variance estimation while making full use of the statistical distribution characteristics of system information. However, due to the non-recursive characteristics of Wiener filtering and the limitations of being only suitable for station...

Claims

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Application Information

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IPC IPC(8): G06F30/367
CPCG06F30/367
Inventor 王秋鹏孙晓辉文成林徐晓滨袁洢苒
Owner HANGZHOU DIANZI UNIV
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