Supercharge Your Innovation With Domain-Expert AI Agents!

Multi-target track extraction method based on Gaussian mixture probability hypothesis density filter

A technology of Gaussian mixture probability and hypothesis density, applied in the field of multi-target tracking, can solve the problem of filter performance degradation

Active Publication Date: 2020-10-23
ZHEJIANG UNIV OF TECH
View PDF10 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, in order to solve the problem of filter performance degradation when the clutter density is too high, the present invention updates the weight by modifying the Gaussian component, and extracts the observation value uniquely corresponding to the target, so as to ensure the continuity of the 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-target track extraction method based on Gaussian mixture probability hypothesis density filter
  • Multi-target track extraction method based on Gaussian mixture probability hypothesis density filter
  • Multi-target track extraction method based on Gaussian mixture probability hypothesis density filter

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0067] The effect of the present invention is further verified and illustrated by the following simulation.

[0068] This simulation environment is built in a two-dimensional plane monitoring area [-1000, 1000]×[-1000, 1000], and the number of targets in this monitoring area is unknown and changes with time. The sensor is located at the point (0,0) in the plane, and its field of view is the monitoring area, and there are clutter and missed detection of the sensor. For simplicity, this paper does not consider the case of derivative targets.

[0069] The state of the target is Where (x, y) is the position of the target, for the target speed. The survival probability P of the target at time k S,k = 0.99. The motion of the target satisfies the CV model, then the target state transition matrix and the process noise covariance matrix are respectively

[0070]

[0071] Among them, the sampling period T=1s, σ v =0.2m / s 2 is the standard deviation of the process noise.

...

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-target track extraction method based on a Gaussian mixture probability hypothesis density filter. The multi-target track extraction method comprises the following stepsof step 1, initializing Gaussian components; step 2, Gaussian component prediction; step 3, Gaussian component updating; step 4, pruning and merging; step 5, state estimation; and step 6, generatinga flight pat. The method is advantaged in that each Gaussian component is endowed with a special label value, the incidence relation between the states, changing along with time, of all the targets isgiven, and finally the tracks of all the targets are extracted, and meanwhile, aiming at a problem that the performance of the filter is reduced under the condition that the clutter concentration istoo high, the observation value uniquely corresponding to the target is extracted by modifying the updating weight of the Gaussian component, so continuity of the tracks is ensured.

Description

technical field [0001] The invention relates to the field of multi-target tracking, in particular to a method for extracting tracks after tracking multiple targets in a clutter environment. Background technique [0002] The multi-target tracking technology aims to analyze the estimated number and trajectory of the target from the measurement information obtained by the sensor. The measurement information includes real target measurement and false alarm measurement caused by clutter and noise. Different from single target tracking, the traditional multi-target tracking method based on data association is not adaptable when tracking multiple targets because the number of targets cannot be known in advance. Mahler et al. proposed a Probability Hypothesis Density (PHD) filtering method based on a strict mathematical theory of random finite sets. By recursively multi-target PHD, the number and location of targets are estimated, and the problem of data association is avoided. How...

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): G01C21/20
CPCG01C21/20
Inventor 赵云波朱创唐敏周庆瑞
Owner ZHEJIANG UNIV OF TECH
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

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

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com