Random system filter containing unknown input and non-Gaussian measurement noise

A technology for measuring noise and unknown input, applied in impedance networks, digital technology networks, electrical components, etc., can solve problems such as limitations, and achieve the effects of enhanced adaptability, high-precision state estimation, and improved robustness

Active Publication Date: 2020-01-10
BEIHANG UNIV
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  • Description
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AI Technical Summary

Problems solved by technology

[0005] The technical solution of the present invention is to provide a novel random anti-interference filter suitable for non-Gaussian systems to solve t

Method used

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  • Random system filter containing unknown input and non-Gaussian measurement noise
  • Random system filter containing unknown input and non-Gaussian measurement noise
  • Random system filter containing unknown input and non-Gaussian measurement noise

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Embodiment Construction

[0041] like figure 1 Shown, the specific implementation steps of the present invention are as follows (the specific realization of method is illustrated with a certain moving body of approximately uniform acceleration rectilinear motion as an example):

[0042] 1. State prediction

[0043] (1) When the moving body is moving in a straight line with approximately uniform acceleration, the kinematic equation is as follows:

[0044]

[0045] The measurement equation is:

[0046] the y k =s k +d k +υ k

[0047] where s k , v k and a k respectively represent the position, velocity and acceleration of the moving body at time k, and the measurement information y k Provided by the position sensor, T is the sampling period, d k Indicates unknown input / disturbance, (i=1,2,3) are independent Gaussian white noise, υ k Denotes zero mean variance for R k The noise of , which has the characteristics of Student's t distribution, Laplace distribution or other distributions wit...

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Abstract

The invention relates to a random system filter containing unknown input and non-Gaussian measurement noise. The method comprises the following steps: firstly, for a discrete time linear stochastic system containing unknown input/interference, completing prediction and estimation of a state based on a state equation; secondly, constructing an index function based on a Huber function instead of a traditional mean square error criterion when interference estimation and state filtering are solved in allusion to the facts that measurement noise of an actual system usually contains more outliers and probability distribution of the noise often has peak, fat tail and other strong non-Gaussian characteristics; thirdly, calculating interference estimation and state filtering estimation based on animmobile point iteration method through minimizing an index function; and finally, carrying out recursion on the state prediction, the interference estimation and the state filtering according to time, and giving a design flow of the filter. The method can be popularized and applied to the fields of integrated navigation, target tracking, signal processing and the like, and solves the problem of high-precision state estimation of an actual system.

Description

technical field [0001] The invention relates to a stochastic system filter containing unknown input / interference and non-Gaussian measurement noise. Aiming at the unknown input or unknown dynamic interference received by the system, the recursive filter structure of state prediction, interference estimation and state filtering is adopted. At the same time, considering the non-Gaussian nature of the measurement noise, the performance index function of interference and state estimation is constructed based on the Huber function, which enhances the robustness of the filter to the measurement outlier. The invention can be applied to the movement of aircraft, ships, vehicles, etc. It can also be applied to target tracking, signal processing and other related fields. Background technique [0002] Aircraft, ships, vehicles and other moving bodies rely on the navigation system to obtain their own motion information and attitude information in real time, and the filtering algorithm i...

Claims

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Application Information

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IPC IPC(8): H03H17/02
CPCH03H17/0211
Inventor 郭雷田波乔建忠李文硕
Owner BEIHANG UNIV
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