• Title/Summary/Keyword: restricted isometry property (RIP)

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Guaranteed Sparse Recovery Using Oblique Iterative Hard Thresholding Algorithm in Compressive Sensing (Oblique Iterative Hard Thresholding 알고리즘을 이용한 압축 센싱의 보장된 Sparse 복원)

  • Nguyen, Thu L.N.;Jung, Honggyu;Shin, Yoan
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39A no.12
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    • pp.739-745
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    • 2014
  • It has been shown in compressive sensing that every s-sparse $x{\in}R^N$ can be recovered from the measurement vector y=Ax or the noisy vector y=Ax+e via ${\ell}_1$-minimization as soon as the 3s-restricted isometry constant of the sensing matrix A is smaller than 1/2 or smaller than $1/\sqrt{3}$ by applying the Iterative Hard Thresholding (IHT) algorithm. However, recovery can be guaranteed by practical algorithms for some certain assumptions of acquisition schemes. One of the key assumption is that the sensing matrix must satisfy the Restricted Isometry Property (RIP), which is often violated in the setting of many practical applications. In this paper, we studied a generalization of RIP, called Restricted Biorthogonality Property (RBOP) for anisotropic cases, and the new recovery algorithms called oblique pursuits. Then, we provide an analysis on the success of sparse recovery in terms of restricted biorthogonality constant for the IHT algorithms.

Multiple Candidate Matching Pursuit (다중 후보 매칭 퍼슛)

  • Kwon, Seokbeop;Shim, Byonghyo
    • Journal of Broadcast Engineering
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    • v.17 no.6
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    • pp.954-963
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    • 2012
  • As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention. In this paper, we multiple candidate matching pursuit (MuCaMP), which builds up candidate support set in every iteration and uses the minimum residual at last iteration. Using the restricted isometry property (RIP), we derive the sufficient condition for MuCaMP to recover the sparse signal exactly. The MuCaMP guarantees to reconstruct the K-sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{N+K}<\frac{\sqrt{N}}{\sqrt{K}+3\sqrt{N}}$. In addition, we show a recovery performance both noiseless and noisy measurements.

Generalized Orthogonal Matching Pursuit (일반화된 직교 매칭 퍼슛 알고리듬)

  • Kwon, Seok-Beop;Shim, Byong-Hyo
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.49 no.2
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    • pp.122-129
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    • 2012
  • As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we present an extension of OMP for pursuing efficiency of the index selection. Our approach, referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple (N) columns are identified per step. Using the restricted isometry property (RIP), we derive the condition for gOMP to recover the sparse signal exactly. The gOMP guarantees to reconstruct sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{NK}$ < $\frac{\sqrt{N}}{\sqrt{K}+2\sqrt{N}}$. In addition, we show recovery performance and the reduced number of iteration required to recover the sparse signal.

Novel Transmission System of 3D Broadcasting Signals using Compressed Sensing (압축 센싱을 이용한 3D 방송 신호 전송 시스템)

  • Lee, Sun Yui;Cha, Jae Sang;Park, Gooman;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.8 no.4
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    • pp.130-134
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    • 2013
  • This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduce the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept was described. Recently proposed CS algorithm AMP(Approximate Message Passing) and CoSaMP(Compressive Sampling Matched Pursuit) was described. Image data that compressed and restored by these algorithm was compared. Calculation time of the algorithm having a low complexity is determined.

Sampling Techniques for Wireless Data Broadcast in Communication (통신에서의 무선 데이터 방송을 위한 샘플링 기법)

  • Lee, Sun Yui;Park, Gooman;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.10 no.3
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    • pp.57-61
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    • 2015
  • This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduces the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept was described. CS algorithm SS-CoSaMP(Single-Space Compressive Sampling Matched Pursuit) and AMP(Approximate Message Passing) was described. Image data compressed and restored by these algorithm was compared. Calculation time of the algorithm having a low complexity is determined.

Novel Compressed Sensing Techniques for Realistic Image (실감 영상을 위한 압축 센싱 기법)

  • Lee, Sun Yui;Jung, Kuk Hyun;Kim, Jin Young;Park, Gooman
    • Journal of Satellite, Information and Communications
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    • v.9 no.3
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    • pp.59-63
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    • 2014
  • This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduces the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept were described. Recently proposed CS algorithm AMP(Approximate Message Passing) and CoSaMP(Compressive Sampling Matched Pursuit) were described. This paper compared an accuracy between two algorithms and a calculation time that image data compressed and restored by these algorithms. As result determines a low complexity algorithm for 3D broadcast system.

Compressed Sensing Techniques for Video Transmission of Multi-Copter (멀티콥터 영상 전송을 위한 압축 센싱 기법)

  • Jung, Kuk Hyun;Lee, Sun Yui;Lee, Sang Hwa;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.9 no.2
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    • pp.63-68
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    • 2014
  • This paper proposed a novel compressed sensing (CS) technique for an efficient video transmission of multi-copter. The proposed scheme is focused on reduction of the amount of data based on CS technology. First, we describe basic principle of Spectrum sensing. And then we compare AMP(Approximate Message Passing) with CoSaMP(Compressive Sampling Matched Pursuit) through mathematical analysis and simulation results. They are evaluated in terms of calculation time and complexity, then the promising algorithm is suggestd for multicopter operation. The result of experiment in this paper shows that AMP algorithm is more efficient than CoSaMP algorithm when it comes to calculation time and image error probability.