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

Compressed sensing reconstructing method of sparse signal with unknown block sparsity

A technology for compressive sensing reconstruction and sparse signal, applied in the field of compressive sensing, which can solve the problem of inability to reconstruct

Inactive Publication Date: 2013-03-20
HARBIN INST OF TECH
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the over-matching phenomenon in the existing block sparse signal matching tracking reconstruction method, and the unknown block sparsity K The problem that cannot be reconstructed when the block sparsity is unknown, a sparse signal compression sensing reconstruction method with unknown block sparsity is proposed

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
  • Compressed sensing reconstructing method of sparse signal with unknown block sparsity
  • Compressed sensing reconstructing method of sparse signal with unknown block sparsity
  • Compressed sensing reconstructing method of sparse signal with unknown block sparsity

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0010] in, d is a block vector Group The length of the sub-block, set the initial value of the residual r 0 = y , restore the matrix , the step size step =1, signal support set size S = k , the source signal x The reconstruction vector of ;

specific Embodiment approach 2

specific Embodiment approach 3

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 relates to a compressed sensing reconstructing method of a sparse signal with the unknown block sparsity, belonging to the technical field of compressed sensing, in particular to a reconstruction method of a block sparse signal. The method comprises the steps of finding out one subset of a signal support set by initializing block sparsity k and iterating each block sparse signal, increasing the block sparsity while keeping iteration and finally finding out the support set of the whole source signal x so as to achieve the purpose of reconstructing the source signal x. The invention has high reconstruction precision by iterating and modifying the support set many times, and has high probability for reconstructing block sparse signals without the overmatching phenomenon compared with the traditional block sparsity matching and tracking and orthogonal matching and tracking method. The invention does not need the block sparsity as the priori knowledge and is particularly suitable for the reconstruction field of signals with unknown block sparsity.

Description

technical field [0001] The invention relates to the technical field of compressed sensing, in particular to a reconstruction method for block sparse signals. Background technique [0002] The traditional signal sampling theory is based on the Nyquist sampling theorem, that is, in order to ensure that the information of the source signal is not lost and to restore the source signal without distortion, the sampling rate needs to be at least twice the signal bandwidth. This often requires a high sampling rate for the digitization of broadband analog signals, which increases the burden on physical devices. And for signals with a large amount of data, the storage capacity and processing speed are further limited. [0003] Compressed Sensing (CS) is a brand-new signal sampling theory proposed in 2004. Its idea is to observe the signal globally at a speed much lower than the Nyquist sampling rate for sparse signals, and then pass appropriate re- The construction algorithm reconst...

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): H03M7/30
Inventor 付宁乔立岩马云彤曹离然彭喜元
Owner HARBIN INST OF TECH
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