Parallel computing particle probability hypothesis density filtering multi-target tracking method

A probability hypothesis density and multi-target tracking technology, which is applied in radar and sonar signal processing and image fields, can solve the problems that particle probability hypothesis density filtering cannot realize complete parallel computing and poor real-time performance

Active Publication Date: 2019-06-04
WUHAN UNIV OF SCI & TECH
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a multi-target tracking method for parallel computing particle probability hypothesis density filtering, which solves the problem that the above particle probability hypothesis density filtering cannot realize complete parallel computing and poor re

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
  • Parallel computing particle probability hypothesis density filtering multi-target tracking method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0015] In order to further illustrate the technical scheme of the present invention, below in conjunction with figure 1 To describe the present invention in detail, but not to limit the present invention.

[0016] Combined with the accompanying drawings, figure 1 Among them, CU is a central processing unit, PE is a processing unit, and 1 CU and multiple PEs form a parallel processing unit. The present invention specifically includes the following steps:

[0017] Step 1, processor parallel task allocation: the multi-core processor undertakes the calculation task of parallel particle probability hypothesis density filtering multi-target tracking, and divides the multi-core processor into a central processing unit (CU, Central Unit) and P processing units ( PE, Processing Element);

[0018] Step 2, initialization: the CU distributes the initialized or last iteration L particles to P PEs on average, and each PE processes L / P particles independently, and the CU inputs all observa...

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 parallel computing particle probability hypothesis density filtering multi-target tracking method. In view of the problems that the traditional particle probability hypothesis density filtering can not achieve complete parallel computing and the real-time performance is poor, a multi-core processor is used to realize parallel computing of all processes from importance sampling to a state estimation stage, so as to improve the real-time performance of multi-target tracking. Each parallel processing unit obtains initialized particles and then independently performs importance sampling, weight calculation normalization, resampling and local estimation, the local weight of the resampled particle before resampling is kept and recorded, a central unit calculates the particle weight ratio of each parallel unit participating in the state estimation according to the kept local weight, and then, the local estimation value is weighted and fused according to the weight ratio to obtain final multi-target state global estimation. The real-time performance of multi-target tracking is improved through parallel computing, and the application prospect is good.

Description

technical field [0001] The invention relates to the field of image, radar and sonar signal processing. Background technique [0002] Multi-target tracking is the use of signal processing methods to estimate the state information of multiple targets in image, radar, sonar and other applications. It has been widely used in military and civilian fields, and is a very popular research field in the world. one. [0003] The state estimation method based on the probability hypothesis density (Probability Hypothesis Density, PHD) filter and various improved methods has been most widely used in multi-target tracking. The sequential Monte Carlo implementation method of the PHD filter, that is, the particle Particle-Probability Hypothesis Density (P-PHD) filtering, realizes the recursion of PHD in the form of particles, and uses a set of weighted random samples to approximate the probability hypothesis density distribution, so as to solve the calculation problem of multiple integrals ...

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): G01S7/41G01S7/539G01S13/66G01S15/66
CPCY02D10/00
Inventor 左韬陶强汤泉闵华松
Owner WUHAN UNIV OF SCI & TECH
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