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A Robust Filtering Method Against Parameter Uncertainty and Observation Loss

A technology of parameter uncertainty and robust filtering, applied in the field of robust filtering, can solve problems such as large estimation error, packet loss of main inertial navigation information, estimation divergence, etc., and achieve the effect of good robustness and wide application background

Active Publication Date: 2016-04-06
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

The above requirements limit the application of the Kalman filter method to a large extent.
In practice, various complex systems often do not meet the requirements for the use of Kalman filtering. For example, in the transfer alignment, the main inertial navigation information packet is lost due to communication delay, and in the INS / GPS integrated navigation system, due to the GPS signal Lost observation due to occlusion
At this time, continuing to use the traditional Kalman filter estimation error will be large and may even lead to estimation divergence

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Embodiment Construction

[0018] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0019] The basic principle of the present invention is: when there is uncertainty in the system, it is no longer possible to obtain accurate estimation error covariance, instead, for the situation where the parameter uncertainty norm is bounded, the method of linear matrix inequality can be used Find an upper bound on the estimated error covariance. The upper bound is a function of the filter parameters, and the upper bound of the estimated error covariance is minimized by designing the filter parameters. The present invention is also applicable when there is observation loss in the system. In this embodiment, a simple speed-position system is considered, the initial position and speed of the moving body are 1m and 1m / s respectively, artificial interference is imposed on the maneuvering of the moving body, the position of the moving body is ...

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Abstract

The invention provides a robust filtering method aiming at parameter uncertainty and observation loss. The robust filtering method comprises the following steps: 1, describing a question through a mathematical model, and modeling of norm-bounded parameter uncertainty and observation loss; 2, giving a filter model of undetermined parameters according to the mathematical model established in the step 1; 3, performing augmentation on an original state equation, solving a state covariance matrix after the augmentation, and constructing a novel state vector; 4, searching for an estimation error covariance of the original state vector according to the state covariance matrix after the augmentation in the step 3; and 5, setting the filter parameters, so that a minimum value is obtained at the upper bound of the estimation error covariance obtained in the step 4. Therefore, minimization of the upper bound of the state estimation error covariance is realized under the condition that a filter has the norm-bounded parameter uncertainty and observation loss.

Description

technical field [0001] The invention relates to a robust filtering method for parameter uncertainty and observation loss, which is suitable for various system state estimations including pan-number bounded parameter uncertainty or observation loss, and belongs to the field of system state estimation. Background technique [0002] State estimation is the core problem of realizing the performance of many practical systems. The estimated result is usually used as the final output of the system or a control signal to regulate the system output. The accuracy of the estimation largely determines the performance metrics of the system. The estimation accuracy is mainly affected by the accuracy of system modeling, the accuracy of sensors and the effectiveness of observation signals. The design principle of the state estimator is to estimate the system state faster and more accurately under the same conditions. [0003] Currently existing state estimation methods: Kalman filter, as...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 付梦印王博邓志红丰璐
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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