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

Generalized ROMP (Regularized Orthogonal Matching Pursuit) method for reconstructing radar signals

An orthogonal matching tracking and radar signal technology, which is applied to radio wave measurement systems, instruments, etc., can solve the problems of slow iterative convergence and low precision of radar signal data recovery, and achieve fast iterative convergence, short calculation time, and high precision effect

Inactive Publication Date: 2017-05-24
HARBIN INST OF TECH
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the iterative convergence speed is slow and the recovery accuracy of radar signal data is low when the missing part of radar signal data is continuous and the missing part of radar signal data is large, and proposes a generalized regularization method for reconstructing radar signal Orthogonal Matching Pursuit

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
  • Generalized ROMP (Regularized Orthogonal Matching Pursuit) method for reconstructing radar signals
  • Generalized ROMP (Regularized Orthogonal Matching Pursuit) method for reconstructing radar signals
  • Generalized ROMP (Regularized Orthogonal Matching Pursuit) method for reconstructing radar signals

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0023] Specific implementation mode 1: The specific process of a generalized regularized orthogonal matching and tracking method for reconstructing radar signals in this implementation mode is as follows:

[0024] Step 1. Initialize the matrix composed of the residual error of the radar signal to be reconstructed, the iterative index set, and the column vector corresponding to the index, and obtain the initial value of the matrix composed of the residual error of the radar signal to be reconstructed, the iterative index set, and the column vector corresponding to the index ;

[0025] Step 2. Calculate the inner product of the recovery matrix A of the radar signal to be reconstructed and the initial value of the residual of the radar signal to be reconstructed, and select the atom J according to the regularization standard 0 ;

[0026] Step 3. From atom J 0 Take the first S maximum values ​​to form the corresponding set J 0 ’; according to the set J 0 'Select the correspond...

specific Embodiment approach 2

[0032] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the step 1, the residual error of the radar signal to be reconstructed, the iterative index set, and the matrix composed of the column vector corresponding to the index are respectively initialized to obtain the to-be-reconstructed The initial value of the matrix composed of the residual of the radar signal, the iterative index set, and the column vector corresponding to the index; the specific process is:

[0033] Initialize the residual r of the radar signal to be reconstructed 0 =y, iterative index set of the radar signal to be reconstructed The index of the radar signal to be reconstructed corresponds to a matrix composed of column vectors t=0;

[0034] Set the residual error of the radar signal to be reconstructed for t iterations as r t , the index (column number) set of the radar signal to be reconstructed in t iterations is Λ t , t represents the number of it...

specific Embodiment approach 3

[0042] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the inner product of the restoration matrix A of the radar signal to be reconstructed and the residual initial value of the radar signal to be reconstructed is calculated in the second step, according to The regularization criterion selects the atom J 0 ; The specific process is:

[0043] Calculate the inner product of the recovery matrix A of the radar signal to be reconstructed and the initial value of the residual, u=abs[A T r t-1 ], i.e. compute t - 1 ,a j >

[0044] Select the first K maximum values ​​or all non-zero values ​​in u (if the number of non-zero values ​​is less than K), these values ​​correspond to the column number j of the recovery matrix A to form a set J (column number set), in the set J according to the regularization Standard Select Atom J 0 , to meet the requirements: |u(i)|≤2|u(j)|, i,j∈J 0 , select the atom J that satisfies the requi...

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 generalized ROMP (Regularized Orthogonal Matching Pursuit) method for reconstructing radar signals and aims to solve the problems that when data missing parts of the radar signals are continuous and the radar signals data are missing more, the iterative convergence speed is slower, and the data recovery accuracy of the radar signals are low. The generalized ROMP method comprises the specific process of 1, obtaining initial values of a residual error, an iterative index set and a matrix formed by column vectors corresponding to indexes; 2, computing an inner product of A and the initial value of the residual error, and selecting atoms; 3, taking the first S maximum values from the atoms to form J0'; selecting corresponding aj in the A according to the J0', and updating At; solving the least square solution of an equation; 5, updating the residual error, judging whether an iteration condition is met or not, executing step 6 if the iteration condition is met, and executing the step 2 if the iteration condition is not met; 6, obtaining all reconstructural nonzero terms in the position of the lambda t, and obtaining reconstructed radar signals. The generalized ROMP method disclosed by the invention is used for the field of reconstruction of the radar signals.

Description

technical field [0001] The invention relates to a generalized regularized orthogonal matching pursuit method. Background technique [0002] Classical greedy algorithms such as orthogonal matching pursuit algorithm are often used to restore missing data, and perform compressed sensing processing on radar signal data that meets a certain degree of sparsity, so as to restore the original radar signal data to the maximum extent and meet certain data recovery requirements. precision. Common greedy algorithms include orthogonal matching pursuit algorithm (orthogonal matching pursuit OMP), regularized orthogonal matching pursuit algorithm (Regularized OMP, ROMP), compressed sampling matching pursuit algorithm (Compressive Sampling MP, CoSAMP) and subspace pursuit algorithm (Subspace pursuit) SP) etc. Although these common algorithms can restore the missing radar signal data to a certain extent, when the missing part of the radar signal data is continuous and the missing radar sig...

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 Applications(China)
IPC IPC(8): G01S7/02
CPCG01S7/02
Inventor 季柄任王勇赵彬许荣庆
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

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