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Multi-model self calibration expansion Kalman filtering method

An extended Kalman and self-calibration technology, applied in the field of robust Kalman filtering, can solve problems such as the inability to deal with the influence of unknown input of nonlinear systems

Inactive Publication Date: 2018-01-05
BEIHANG UNIV
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Problems solved by technology

However, although the multi-model self-calibration Kalman filter method ensures the accuracy of the unknown input non-zero segment filtering and improves the accuracy of the unknown input as the zero segment, it is only suitable for linear systems and cannot deal with the nonlinearity that is common in engineering practice. Problems where the system is affected by unknown inputs

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  • Multi-model self calibration expansion Kalman filtering method

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Embodiment Construction

[0103] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0104] The present invention proposes a multi-model self-calibration extended Kalman filter method, the flow chart of which is as follows figure 1 As shown, the time update flow chart is as follows figure 2 As shown, it includes the following seven steps:

[0105] Step 1: Establish the basic equations of the system

[0106]

[0107]

[0108] Z k = h k (X k )+V k (39)

[0109] In the formula, X k represents the state vector of the system, and Corresponding to the kinetic model with unknown input and the standard kinetic model, Z k represents the system measurement vector, f k ( ) and h k (·) are nonlinear state recurrence equation and measurement equation respectively, b k Indicates unknown input, W k with V k are the system noise vector and the measurement noise vector respectively, and their variance matrices are Q k and R k , and satisfy ...

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Abstract

The invention provides a multi-model self-calibration extended Kalman filter method, the steps are as follows: one: establish the basic equation of the system; two: perform linearization processing on the system composed of formula (1), formula (2) and formula (3) ;3: Filter initialization of the system; 4: Time update of the system; 5: Iterative variable update; 6: Measurement update; 7: Iterative calculation; The problem affected by the unknown input is solved, and at the same time, since the unknown input is considered to be zero, the adaptability to the complex environment is improved compared with the self-calibration extended Kalman filtering method; and because the present invention is based on the extended Kalman filter The Mann filtering method does not need to complete the information transmission in the form of sampling, so its operation speed for dealing with nonlinear systems is faster than other nonlinear filtering methods, and it is convenient for practical engineering applications.

Description

【Technical field】 [0001] The invention provides a multi-model self-calibration extended Kalman filter method, which belongs to the technical field of robust Kalman filter. 【Background technique】 [0002] Kalman filtering is a method of estimating the state of the system by using the system state equation and measurement equation. It has been widely used in the engineering field since it was proposed in 1960. Filtering methods, they all require the system equations to be exact. However, in engineering practice, due to the influence of environmental factors, improper selection of models and parameters, etc., the system state equation is often disturbed by unknown inputs, which reduces the filtering accuracy and even leads to filtering divergence. Aiming at this problem, the literature "Self-calibration Kalman filter method [J]. Journal of Aerodynamics. 2014, 29 (06): 1363-1368" proposed a self-calibration Kalman filter method (Self-calibration KalmanFilter, SKF), the The met...

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Application Information

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IPC IPC(8): H03H17/02
Inventor 杨海峰傅惠民张勇波王治华肖梦丽崔轶
Owner BEIHANG UNIV
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