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

Multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method

A multi-target tracking and multi-sensor technology, which is applied in the field of multi-sensor GM-PHD adaptive sequential fusion multi-target tracking, achieves the effects of small calculation amount, wide application and clear configuration structure

Pending Publication Date: 2021-11-19
HANGZHOU DIANZI UNIV
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] An object of the present invention is to address the deficiencies in the prior art, and propose a multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method based on cumulative amplitude likelihood, which solves the problem of multiple targets in dense clutter environments. An optimization problem for sensor fusion sequence that improves target estimation accuracy and maintains track

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
  • Multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method
  • Multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method
  • Multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0019] The specific implementation manner of the present invention will be described in detail below in combination with the technical scheme and accompanying drawings.

[0020] Such as figure 1 As shown, the multi-sensor GM-PHD adaptive sequential fusion method based on cumulative magnitude likelihood is characterized in that the method specifically includes the following steps:

[0021] Step (1), build a multi-sensor multi-target tracking scene, and initialize the motion model of the target, set the relevant parameters of the target motion, including the process noise of the target motion and the measurement noise of the sensor; wherein the measurement of the sensor comes from the target or from clutter;

[0022] The motion model under the linear discrete system is expressed as:

[0023] x k = F x k-1 +B·μ k +ω k (1)

[0024] where x k is the state vector of the target at time k, F is the target state transition matrix, B is the control matrix, μ k is the motion co...

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 multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method. In order to solve the problem of multi-sensor multi-target tracking under dense clutters, the invention provides a set of complete processing method flow, amplitude information is introduced into a GM-PHD filter, original measurement is screened through amplitude characteristics of targets and clutters and by setting a detection threshold, a large amount of clutters are eliminated, effective measurement is obtained, the invention further provides a fusion sequence optimization method based on the effective measurement cumulative amplitude likelihood ratio, and multi-sensor fusion is carried out in combination with a distributed sequential fusion framework. The method is clear in configuration structure and small in calculation amount, and can be widely applied to the field of multi-target tracking.

Description

technical field [0001] The invention belongs to the multi-target tracking technology field of multi-sensor fusion in a complex environment, and relates to a multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method, specifically a multi-sensor adaptive fusion multi-target based on probability assumption density filtering The tracking method is used to solve multi-target tracking under dense clutter, improve the tracking effect of unknown targets in the monitoring area, and achieve high-precision and stable tracking results. Background technique [0002] In a multi-sensor tracking system, data fusion technology needs to fuse data from multiple sensors to obtain state estimation of the target, which can improve the performance of the tracking system. However, with the increase in the number of targets and the complexity of data association, multi-sensor multi-target tracking technology also faces many challenges. So far, domestic and foreign researchers have...

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): G06K9/62G06K9/00G06F30/20
CPCG06F30/20G06F2218/02G06F18/25Y02D30/70
Inventor 申屠晗张浩野荣英姣郭云飞彭冬亮
Owner HANGZHOU DIANZI 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