A Study on Sparse Recovery Algorithms for Compressed Sensing = 압축센싱을 위한 희소복원 알고리즘에 관한 연구

김준한 2022년
논문상세정보
' A Study on Sparse Recovery Algorithms for Compressed Sensing = 압축센싱을 위한 희소복원 알고리즘에 관한 연구' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 응용 물리
  • compressed sensing
  • orthogonal least squares
  • orthogonal matching pursuit
  • restricted isometry property
  • sparse signal recovery
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
4,707 0

0.0%

' A Study on Sparse Recovery Algorithms for Compressed Sensing = 압축센싱을 위한 희소복원 알고리즘에 관한 연구' 의 참고문헌

  • [6] C. Soussen, R. Gribonval, J. Idier, and C. Herzet, “Joint k-step analysis of orthogonal matching pursuit and orthogonal least squares,” IEEE Trans. Inform. Theory, vol. 59, no. 5, pp. 3158–3174, May 2013.
    [2013]
  • [60] T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communi- cations for 5G cellular: It will work!,” IEEE Access, vol. 1, pp. 335–349, May 2013.
    [2013]
  • [5] J. W. Choi, B. Shim, Y. Ding, B. Rao, and D. I. Kim, “Compressed sensing for wireless communications: Useful tips and tricks,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1527–1550, 3rd Quart., 2017.
    [2017]
  • [58] J. Kim, J.Wang, and B. Shim, “Optimal restricted isometry condition of normalized sampling matrices for exact sparse recovery with orthogonal least squares,” IEEE Trans. Signal Process., vol. 69, pp. 1521–1536, Feb. 2021.
  • [54] S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing, in Applied and Numerical Harmonic Analysis, Birkhauser, 2013.
    [2013]
  • [52] Q. Mo, “A sharp restricted isometry constant bound of orthogonal matching pursuit,” arXiv:1501.01708, 2015.
    [2015]
  • [4] V. Papyan, Y. Romano, J. Sulam, and M. Elad, “Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks,” IEEE Signal Process. Mag., vol. 35, no. 4, pp. 72–89, Jul. 2018.
    [2018]
  • [45] R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Construct. Approx., vol. 28, no. 3, pp. 253–263, Dec. 2008.
    [2008]
  • [41] E. J. Candès and T. Tao, “Decoding by linear programming,” IEEE Trans. Inform. Theory, vol. 51, no. 12, pp. 4203–4215, Dec. 2005.
    [2005]
  • [40] E. J. Candès, “The restricted isometry property and its implications for compressed sensing,” Comptes Rendus Mathematique, vol. 346, no. 9-10, pp. 589– 592, May 2008.
    [2008]
  • [35] Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition,” in Proc. 27th Annu. Asilomar Conf. Signals, Syst., and Comput., Pacific Grove, CA, USA, Nov. 1993, pp. 40–44.
    [1993]
  • [2] E. J. Candès and M. B.Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21–30, Mar. 2008.
    [2008]
  • [26] S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” IEEE Trans. Signal Process., vol. 53, no. 7, pp. 2477–2488, Jul. 2005.
    [2005]
  • [22] P. Feng and Y. Bresler, “Spectrum-blind minimum-rate sampling and reconstruction of multiband signals,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Atlanta, GA, USA, May 1996, pp. 1668–1691.
    [1996]
  • [1] J. Kim and B. Shim, “Multiple orthogonal least squares for joint sparse recovery,” in Proc. IEEE Int. Symp. Inform. Theory, Vail, CO, USA, Jun. 2018, pp. 61–65.
    [2018]
  • [16] T. Blumensath and M. E. Davies, “On the difference between orthogonal matching pursuit and orthogonal least squares,” Tech. Rep., 2007. [Online]. Available: https://eprints.soton.ac.uk/142469/1/BDOMPvsOLS07.pdf
    [2007]
  • Theoretical results on sparse representations of multiplemeasurement vectors
    J. Chen and X. Huo , vol . 54 , no . 12 , pp . 4634 ? 4643 [2006]
  • The approximation of one matrix by another of lower rank
    C. Eckart and G. Young vol . 1 , no . 3 , pp . 211 ? 218 [1936]
  • Support recovery with orthogonal matching pursuit in the presence of noise
    J. Wang vol . 63 , no . 21 , pp . 5868 ? 5877 [2015]
  • Support recovery of sparse signals in the presence of multiple measurement vectors
    Y. Jin and B. D. Rao vol . 59 , no . 5 , pp . 3139 ? 3157 [2013]
  • Sufficient conditions for generalized orthogonal matching pursuit in noisy case
    B. Li , Y. Shen , Z.Wu , and J. Li , vol . 108 , pp . 111 ? 123 [2015]
  • Subspace pursuit for compressive sensing signal reconstruction
    W. Dai and O. Milenkovic vol . 55 , no . 5 , pp . 2230 ? 2249 [2009]
  • Subspace methods for joint sparse recovery
    K. Lee , Y. Bresler , and M. Junge , vol . 58 , no . 6 , pp . 3613 ? 3641 [2012]
  • Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition
    L. Wang , X. Wang , and J. Feng , vol . 39 , no . 3 , pp . 456 ? 464 [2006]
  • Statistical recovery of simultaneously sparse timevarying signals from multiple measurement vectors
    J. W. Choi and B. Shim vol . 63 , no . 22 , pp . 6136 ? 6148 [2015]
  • Sparse recovery with orthogonal matching pursuit under RIP
    T. Zhang vol . 57 , no . 9 , pp . 6215 ? 6221 [2011]
  • Some new results about sufficient conditions for exact sparse recovery of sparse signals via orthogonal matching pursuit
    C. Liu , Y. Fang , and J. Liu , vol . 65 , no . 17 , pp . 4511 ? 4524 [2017]
  • Signal recovery from random measurements via orthogonal matching pursuit
    J . A. Tropp and A. C. Gilbert , vol . 53 , no . 12 , pp . 4655 ? 4666 [2007]
  • Recovery of sparse signals via generalized orthogonal matching pursuit : A new analysis
    J.Wang , S. Kwon , P. Li , and B. Shim , vol . 64 , no . 4 , pp . 1076 ? 1089 [2016]
  • Recovery of sparse signals using multiple orthogonal least squares
    J. Wang and P. Li , vol . 65 , no . 8 , pp . 2049 ? 2062 [2017]
  • Recovery guarantees for rank aware pursuits
    J. D. Blanchard and M. E. Davies , vol . 19 , no . 7 , pp . 427 ? 430 [2012]
  • Rank awareness in joint sparse recovery
    M. E. Davies and Y. C. Eldar vol . 58 , no . 2 , pp . 1135 ? 1146 [2012]
  • Orthogonal matching pursuit under the restricted isometry property ,
    A. Cohen , W. Dahmen , and R. DeVore , vol . 45 , no . 1 , pp . 113 ? 127 [2017]
  • Orthogonal matching pursuit for sparse signal recovery with noise
    T. T. Cai and L. Wang , vol . 57 , no . 7 , pp . 4680 ? 4688 [2011]
  • Orthogonal least squares methods and their application to non-linear system identification
    S. Chen , S. A. Billings , andW . Luo , vol . 50 , no . 5 , pp . 1873 ? 1896 [1989]
  • Optimized orthogonal matching pursuit approach
    L. Rebollo-Neira and D. Lowe , vol . 9 , no . 4 , pp . 137 ? 140 [2002]
  • On the recovery limit of sparse signals using orthogonal matching pursuit
    J. Wang and B. Shim vol . 60 , no . 9 , pp . 4973 ? 4976 [2012]
  • On angles between subspaces of a finite dimensional inner product space
    P. A. Wedin pp . 263 ? 285 [1983]
  • Nearly optimal restricted isometry condition for rank aware order recursive matching pursuit ,
    J. Kim , J. Wang , and B. Shim , vol . 67 , no . 17 , pp . 4449 ? 4463 [2019]
  • Nearly optimal bounds for orthogonal least squares ,
    J. Wen , J. Wang , and Q. Zhang vol . 65 , no . 20 , pp . 5347 ? 5356 [2017]
  • Multipath matching pursuit
    S. Kwon , J. Wang , and B. Shim vol . 60 , no . 5 , pp . 2986 ? 3001 [2014]
  • Joint sparse recovery using signal space matching pursuit
    J. Kim , J. Wang , L. T. Nguyen , and B. Shim , vol . 66 , no . 8 , pp . 5072 ? 5096 , [2020]
  • Improved bounds on restricted isometry constant for orthogonal matching pursuit ,
    J. Wen , X. Zhu , and D. Li , vol . 49 , no . 23 , pp . 1487 ? 1489 , Nov. [2013]
  • Generalized orthogonal matching pursuit
    J. Wang , S. Kwon , and B. Shim vol . 60 , no . 12 , pp . 6202 ? 6216 [2012]
  • Further results on the subspace distance
    X . Sun , L.Wang , and J. Feng , vol . 40 , no . 1 , pp . 328 ? 329 [2007]
  • From theory to practice : Sub-Nyquist sampling of sparse wideband analog signals
    M. Mishali and Y. C. Eldar , vol . 4 , no . 2 , pp . 375 ? 391 [2010]
  • Exact recovery of sparse signals using orthogonal matching pursuit : How many iterations do we need ?
    J.Wang and B. Shim vol . 64 , no . 16 , pp . 4194 ? 4202 [2016]
  • Compressive diffuse optical tomography : Noniterative exact reconstruction using joint sparsity ,
    O. Lee , J. M. Kim , Y. Bresler , and J. C. Ye , vol . 30 , no . 5 , pp . 1129 ? 1142 [2011]
  • Compressive MUSIC : Revisiting the link between compressive sensing and array signal processing
    J. M. Kim , O. K. Lee , and J. C. Ye , vol . 58 , no . 1 , pp . 278 ? 301 [2012]
  • Compressed sensing
    D. L. Donoho vol . 52 , no . 4 , pp . 1289 ? 1306 [2006]
  • CoSaMP : Iterative signal recovery from incomplete and inaccurate samples
    D. Needell and J . A. Tropp vol . 26 , no . 3 , pp . 301 ? 321 [2009]
  • Analysis of orthogonal matching pursuit using the restricted isometry property ,
    M. A. Davenport and M. B. Wakin vol . 56 , no . 9 , pp . 4395 ? 4401 [2010]
  • An information-theoretic study for joint sparsity pattern recovery with different sensing matrices
    S. Park , N. Y. Yu , and H. N. Lee vol . 63 , no . 9 , pp . 5559 ? 5571 [2017]
  • An improved RIP-based performance guarantee for sparse signal recovery via orthogonal matching pursuit
    L. Chang and J. Wu , vol . 60 , no . 9 , pp . 5702 ? 5715 [2014]
  • Algorithms for simultaneous sparse approximation . part II : Convex relaxation ,
    J . A. Tropp vol . 86 , no . 3 , pp . 589 ? 602 , [2006]
  • Algorithms for simultaneous sparse approximation . part I : Greedy pursuit ,
    J . A. Tropp , A. C. Gilbert , and M. J. Strauss vol . 86 , no . 3 , pp . 572 ? 588 [2006]
  • A sharp condition for exact support recovery with orthogonal matching pursuit
    J.Wen , Z. Zhou , J.Wang , X. Tang , and Q. Mo vol . 65 , no . 6 , pp . 1370 ? 1382 [2017]
  • A remark on the restricted isometry property in orthogonal matching pursuit algorithm
    Q. Mo and Y. Shen , vol . 58 , no . 6 , pp . 3654 ? 3656 [2012]
  • A near-optimal restricted isometry condition of multiple orthogonal least squares
    J. Kim and B. Shim , vol . 7 , no . 1 , pp . 46822 ? 46830 [2019]
  • A hybrid RF/baseband precoding processor based on parallel-index-selection matrix-inversion-bypass simultaneous orthogonal matching pursuit for millimeter wave MIMO systems
    Y. Y. Lee , C. H. Wang , and Y. H. Huang , vol . 63 , no . 2 , pp . 305 ? 317 [2015]