Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF7 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 the problem of high-precision state estimation of complex systems in view of the existing unknown input filtering method being limited to Gaussian systems.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): H03H17/02
CPCH03H17/0211
Inventor 郭雷田波乔建忠李文硕
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com