Unknown adaptive Kalman filter method for system model

An adaptive Kalman and Kalman filtering technology, which is applied in complex mathematical operations, measuring devices, instruments, etc., can solve the problems of filtering accuracy drop and divergence, and achieve the effect of solving the effect of Kalman filter accuracy drop or even divergence

Inactive Publication Date: 2010-10-06
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0006] In order to overcome the deficiencies of the prior art adaptive Kalman filtering method for the unknown or time-varying state model of the...

Method used

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  • Unknown adaptive Kalman filter method for system model
  • Unknown adaptive Kalman filter method for system model
  • Unknown adaptive Kalman filter method for system model

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Embodiment Construction

[0039] refer to Figure 1~4 . Taking the tracking of a maneuvering target as an example, the concrete steps of the inventive method are as follows:

[0040] Step 1: Establish the motion state equation of the target, assuming that the real motion trajectory of the target is attenuated sinusoidal motion in the x and y directions, and the oscillation frequency in the x direction is a variable frequency. Since the motion of the target cannot be known in advance, it is impossible to accurately describe its motion trajectory when establishing the state equation. Assuming that it moves in a straight line at a uniform speed, the established system state equation is as follows:

[0041] x · x · · y · ...

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Abstract

The invention discloses an unknown adaptive Kalman filter method for a system model, which aims at solving the problem that the filter of the Kalman filter method has reduced accuracy even is diffused under the condition that the system status model is unknown or time-variant. Aiming at the unknown or time-variant condition established by the system status model, information is adopted to calculate filter residues based on Kalman filter; a weighting efficient is calculated according to the filter residues and the ratio of measurement noise covariance; the noise covariance matrix of system status is revised in real time through the weighting efficient, so that the problem of accuracy reduction even diffusion of the Kalman filter caused by the unknown or time-variant system model can be solved effectively. Under equal conditions, the X axle filtering error mean is reduced to 0.0056 from 0.1231 in the prior art, and the Y axle filtering error mean is reduced to 0.0039 from 0.3895 in the prior art.

Description

technical field [0001] The invention relates to an adaptive Kalman filtering method, in particular to an adaptive Kalman filtering method with an unknown or time-varying system state model. Background technique [0002] Generally, Kalman filtering needs to accurately know the state model of the system and the statistical characteristics of the system noise when performing filtering. However, in actual situations, the system state model is often unknown or time-varying. For example, the motion state of an aircraft in the air is time-varying and difficult to predict. Due to the unknown or time-varying nature of the state model of the system, the accuracy of Kalman filtering will be reduced or even cause divergence. [0003] The method to solve the uncertain or time-varying system state model is to use the adaptive Kalman filter algorithm. In recent years, many scholars have conducted in-depth and extensive research on the adaptive Kalman filter algorithm, and the commonly us...

Claims

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Application Information

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IPC IPC(8): G06F17/16G06F17/11G01C21/00
Inventor 卢晓东周军吕春红赵斌
Owner NORTHWESTERN POLYTECHNICAL UNIV
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