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

State estimation method based on novel adaptive high-order unscented Kalman filter

An unscented Kalman and state estimation technology, applied in radio wave measurement systems, instruments, etc., to solve problems such as the need to improve accuracy

Inactive Publication Date: 2019-11-26
HARBIN ENG UNIV
View PDF5 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, none of them are improved in the essential sense, and the accuracy still needs to be improved.

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
  • State estimation method based on novel adaptive high-order unscented Kalman filter
  • State estimation method based on novel adaptive high-order unscented Kalman filter
  • State estimation method based on novel adaptive high-order unscented Kalman filter

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0078] The specific implementation method can be divided into the following steps:

[0079] Step 1: Establish target tracking nonlinear discrete state model and measurement model;

[0080] Step 2: Select the optimal free parameter κ according to the state dimension of the target tracking system;

[0081] Step 3: Establish the adoption point and weight of high-order UT acquisition status;

[0082] Step 4: Pass the sampling points through a nonlinear function, and perform weighting processing to obtain the state one-step prediction and the state one-step prediction covariance matrix;

[0083] Step 5: Bring the optimal adaptive factor into the state and predict the covariance matrix in one step;

[0084] Step 6: Establish high-level UT acquisition and measurement adoption points and weights;

[0085] Step 7: Transfer the sampling points through a nonlinear function, and perform weighting processing to obtain the measurement one-step prediction and measurement one-step predicti...

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 discloses a state estimation method based on novel adaptive high-order unscented Kalman filter, which belongs to the field of high-speed carrier state estimation in passive radar tracking. The method provided by the invention comprises the steps that a non-linear discrete state model and a measurement model of a target tracking system are established; the optimal free parameter k isselected according to the state dimension of the target tracking system; a high-order UT is established to acquire state adoption points and weights; sampling points are transferred through a nonlinear function; the optimal adaptive factor is brought into a state one-step prediction covariance matrix; a high-order UT is established to acquire measurement adoption points and weights; the sampling points are transferred through the nonlinear function; a gain matrix is calculated; and posterior state estimation output and the output of the covariance matrix are carried out. According to the invention, the influence of a strong nonlinear maneuvering target and large mutations on a filter is effectively suppressed; the method has a good influence on different sampling intervals and different corner rates; and the influence of dynamic model errors is reduced.

Description

technical field [0001] The invention belongs to the field of state estimation for high-speed carriers in passive radar tracking, and is based on a novel self-adaptive high-order unscented Kalman filter state estimation method. Background technique [0002] State estimation is the most basic aspect in the field of digital signals, and nonlinear processing is a difficult problem in digital signals, and nonlinear filters are a favorable method for dealing with dynamic systems. Many contributions have been made to deal with nonlinearities. These include the most typical method extended Kalman filter (EKF), which is based on the idea of ​​nonlinear function approximation, and requires to ensure that the nonlinear function is continuously differentiable or differentiable. For this method, Jacobian (Jacobian) is required at the technical level. Matrix solution. In engineering applications, the complexity of solving the Jacobian matrix is ​​extremely high, which is difficult to re...

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): G01S7/41
CPCG01S7/415
Inventor 周卫东侯佳欣田园刘璐周中元单承豪邹涵宋啸宇张聪张杰
Owner HARBIN ENG 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