Robust filtering method aiming at parameter uncertainty and observation loss

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...

AI Technical Summary

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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