Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method

A compression sampling and matching pursuit technology, applied in the field of information and communication

Active Publication Date: 2016-01-27
HARBIN INST OF TECH
View PDF3 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention is to solve the following problems of the existing multi-observation value vector orthogonal matching pursuit algorithm based

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
  • Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method
  • Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method
  • Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0106] Specific implementation mode 1. Combination Image 6 Describe this specific implementation method, multi-observation value vector sparsity adaptive compression sampling matching tracking method, the specific process is: input observation matrix A and frame matrix V, set an appropriate residual threshold θ according to the size of the signal-to-noise ratio, and combine signals according to The approximate range of sparsity sets an appropriate stage number threshold σ.

[0107] initialization order support set Residual R=V, support set candidate set According to the approximate range of signal joint sparsity, set the appropriate number of stages stage and step size step. Computes the 2-norm of each atom (column vector) in the observation matrix. Repeat the following steps until the iteration stop condition is met:

[0108] Use the formula:

[0109] s=stage×step(1)

[0110] Compute the estimated sparsity s for each iteration. Multiply the residual matrix R (initia...

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 multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method, which relates to the technical field of information and communication. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method is provided for solving the problem of recovering an original multiband signal from multiple observed value vectors with unknown sparsity after continuous-limited module conversion through sampling by a modulated broadband converter under an Xampling framework. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method comprises the steps of: conducting self-adaptive estimation on sparsity of a signal; updating the sparsity with a given step length factor through repeated iteration so that the sparsity gradually approaches the actual sparsity of the signal; correcting a support set through a backtracking thought and a minimum mean square criterion; stopping iteration until an residual error is less than a set threshold value; and finally reconstructing an original multiband signal through pseudo inverse operation by utilizing the obtained complete support set. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method can achieve the analog reconstruction of the multiband signal based on compressed sensing.

Description

technical field [0001] The invention relates to the field of information and communication technologies, in particular to a Xampling-based analog signal compression sensing reconstruction method. Background technique [0002] In today's society, with the rapid growth of information demand, the signal carrier frequency is getting higher and higher. According to the traditional signal or image sampling method, only when the sampling rate is not less than twice the highest frequency of the signal (the so-called Nyquist rate), can the original signal be accurately restored from the sample point. This condition makes signal processing require higher and higher sampling frequency, and the processing becomes more and more difficult. At the same time, in practical applications, the redundancy of the signal is often reduced by reorganizing the signal without losing useful information through compression, and the efficiency of signal processing, transmission and storage is improved, ...

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): H03M7/30
Inventor 贾敏史瑶顾学迈郭庆刘晓锋王雪陈子研朱思宇
Owner HARBIN INST OF 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