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

Kernel k-means track correlation method based on kmdl criterion

A track correlation and K-means technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., to achieve high correlation accuracy rate, improve correlation accuracy rate, and investigate the effect of correlation effect

Inactive Publication Date: 2018-01-05
XI AN JIAOTONG UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the defects in the above-mentioned prior art, the object of the present invention is to provide a KMDL criterion-based kernel K-means track correlation method, which can quickly and accurately solve multi-target track correlation in complex environments question

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
  • Kernel k-means track correlation method based on kmdl criterion
  • Kernel k-means track correlation method based on kmdl criterion
  • Kernel k-means track correlation method based on kmdl criterion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The present invention will be further described in detail below in conjunction with specific embodiments, which are for explanation rather than limitation of the present invention.

[0052] The nuclear K-mean track correlation method based on KMDL criterion criterion of the present invention includes the following steps:

[0053] Step 1: Build a typical track correlation scene

[0054] 1. Target measurement generation

[0055] For maneuvering targets, the target motion mode is uncertain, and the motion characteristics are unpredictable. It is difficult to establish a single accurate model for the maneuvering target. Select commonly used typical models from three commonly used target motion models: uniform motion model (CV), uniform acceleration motion model (CA), and uniform turning motion model (CT) to generate target measurement, which is better close to the real motion of the target mode.

[0056] 2. Clutter generation

[0057] In actual applications, in real combat scenarios...

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 kernel K-means track association method based on KMDL criterion criterion, comprising the following steps: Step 1: constructing a typical track association scene; Step 2: using KMDL criterion criterion to determine the number of target tracks; Step 3: Correlate with the kernel K-means algorithm for the track observation scene. The disclosed kernel K-means track correlation method based on the KMDL criterion criterion of the present invention, based on the target state information, combines the KMDL criterion criterion and the kernel K-means algorithm to solve the complex environment (intensive clutter, close to the target, The multi-target track association problem with unknown number of targets). This method makes full use of the target's motion state information, effectively improving the correlation accuracy rate, the correlation criterion is simple and easy to implement, the calculation amount is small, the correlation accuracy rate is high, and it is not sensitive to target crossing, so it is suitable for navigation in dense and cross-target environments. Trace correlation, suitable for engineering implementation.

Description

Technical field [0001] The invention belongs to the technical field of multi-sensor multi-target tracking, and specifically relates to a nuclear K-mean track correlation method based on a KMDL criterion criterion. Background technique [0002] The multi-sensor multi-target tracking system mainly receives the local track information from each sensor system through the data link, and then calculates the core issues such as correlation, registration, and fusion of the local track information to form a coordinated detection and fusion target track. [0003] Multi-sensor cooperative target tracking can achieve precise tracking of targets. In practical applications, there are multiple targets to be tracked. At this time, it is necessary to correctly determine the correspondence between the measurement information received by the sensors and the target of interest. However, due to the clutter generated by the false radiation source, interference clutter and false targets, the uncertainty...

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 Patents(China)
IPC IPC(8): G06F19/00G06K9/62
Inventor 郭文锁朱洪艳韩崇昭吴丹傅娜
Owner XI AN JIAOTONG 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