Extended target tracking method based on variational Bayesian expectation maximization

A variational Bayesian and expectation maximization technology, applied in the field of target tracking, can solve the problem of extended target tracking performance degradation and achieve the effect of improving tracking accuracy

Inactive Publication Date: 2015-07-22
XIDIAN UNIV
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional extended target tracking algorithms deal with the situation where the measurement noise covariance is known. In practice, when the measurement noise covariance is unknown, the tracking performance of the extended target will drop sharply

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
  • Extended target tracking method based on variational Bayesian expectation maximization
  • Extended target tracking method based on variational Bayesian expectation maximization
  • Extended target tracking method based on variational Bayesian expectation maximization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] refer to figure 1 , the specific implementation steps of the present invention include as follows:

[0026] Step 1, at time k=0, initialize the joint probability hypothesis density of target state and measurement noise covariance:

[0027] v 0 ( x , R ) = Σ i = 1 J 0 [ w 0 ( i ) N ( x ; m 0 ( i ) , P 0 ...

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 an extended target tracking method based on variational Bayesian expectation maximization (VBEM) and mainly solves the problem that tracking performance of a target is weakened dramatically under the condition that measurement noise covariance is unknown in the conventional extended target tracking field. The extended target tracking method includes firstly predicting relevant parameters of Gaussian inverse gamma components in joint probability hypothesis density of a target state and the measurement noise covariance; updating the parameters of the Gaussian inverse gamma components; finally acquiring the extended target state and the number by construction and combination. It is proved by simulation experiment that multiple extended targets can be well tracked under the unknown number and the unknown measurement noise covariance, and the extended target tracking method is high in tracking accuracy and can be used for tracking aircrafts and submarine targets.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to a target tracking method, which can be used to track multiple extended targets. Background technique [0002] In the traditional target tracking field, due to the limited resolution of the radar, the target is usually regarded as a point target, that is, each target can only produce one measurement at a time. In recent years, with the development of radar detection technology and the needs of practical applications, more targets are regarded as extended targets, that is, each target can generate multiple measurements at each moment. [0003] In the actual target tracking scene, the number of targets cannot be predicted in advance, so the proposal of random set theory greatly meets the needs of target tracking theory. Among many model assumptions on the target, especially the proposed extended target theory is closer to the needs of the current tracking...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/20
Inventor 李翠芸王晋斌姬红兵王荣
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap