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Dice系数前向预测的快速正交正则回溯匹配追踪算法

陈平平 陈家辉 王宣达 方毅 王锋

陈平平, 陈家辉, 王宣达, 方毅, 王锋. Dice系数前向预测的快速正交正则回溯匹配追踪算法[J]. 电子与信息学报, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
引用本文: 陈平平, 陈家辉, 王宣达, 方毅, 王锋. Dice系数前向预测的快速正交正则回溯匹配追踪算法[J]. 电子与信息学报, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
CHEN Pingping, CHEN Jiahui, WANG Xuanda, FANG Yi, WANG Feng. Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
Citation: CHEN Pingping, CHEN Jiahui, WANG Xuanda, FANG Yi, WANG Feng. Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558

Dice系数前向预测的快速正交正则回溯匹配追踪算法

doi: 10.11999/JEIT230558
基金项目: 国家自然科学基金(62171135,62071131),福建省杰青项目(2022J06010),省教育厅重点攻关项目(2023XQ004),泉州市科技计划项目(2021N050)
详细信息
    作者简介:

    陈平平:男,博士,教授,研究方向为压缩感知、无线通信、信道编码调制、多用户接入

    陈家辉:男,硕士生,研究方向为压缩感知、无线通信、多用户接入

    王宣达:男,硕士生,研究方向为压缩感知、无线通信、多用户接入

    方毅:男,博士,教授,研究方向为信道纠错编码与调制、无线通信

    王锋:男,博士,教授,研究方向为电子与通信技术

    通讯作者:

    方毅 fangyi@gdut.edu.cn

  • 中图分类号: TN911.23

Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction

Funds: The National Natural Science Foundation of China (62171135, 62071131), Fujian Distinguished Talent Project (2022J06010), The Key Project of Education Department (2023XQ004), Quanzhou Sci-Tech Project (2021N050)
  • 摘要: 为了提高压缩感知重构算法的成功率与重构精度,该文提出基于Dice前向预测的正交正则回溯匹配追踪算法 (DLARBOMP)。在该算法中,首先从匹配准则与预选阶段原子选取的角度,利用Dice系数代替原子内积计算相关度,保留原始信号信息的特性,以此选择与残差最匹配的原子,提高算法的重构精度。同时,针对信号重构过程回溯算法的时间过长问题,在每次原子迭代过程中,该文利用正则化选择多个原子而非单个原子,实现重构精度与重构时间的平衡。最后,通过稀疏1维信号与2维图像信号重构的实验结果,显示了所提DLARBOMP算法在1维信号重构时兼顾了性能与效率,在2维压缩图像信号重构时提高其峰值信噪比(PSNR),优于正交匹配追踪(OMP)及其最新改进贪婪类算法。
  • 图  1  压缩感知过程

    图  2  DLARBOMP算法流程

    图  3  N = 256, K = 20, M=80残差值随迭代次数的变化关系

    图  4  维信号重构实验

    图  5  重构概率随稀疏度K变化曲线

    图  6  重构概率随观测次数M变化曲线

    图  7  重构时间对比

    图  8  压缩比为0.5时的压缩重构图像

    表  1  压缩比${M \mathord{\left/ {\vphantom {M N}} \right. } N}$分别为0.3,0.5和0.7时算法重构性能比较

    算法 0.3 0.5 0.7
    PSNR(dB) $\sigma $ PSNR(dB) $\sigma $ PSNR(dB) $\sigma $
    OMP[10] 20.1031 0.1394 26.3683 0.0902 29.9089 0.0574
    LAOMP[14] 21.9710 0.1377 26.8765 0.0847 30.3367 0.0559
    LABOMP[15] 21.9811 0.1370 27.0628 0.0835 30.3590 0.0554
    MMP-IIPMC[28] 22.0009 0.1318 27.1078 0.0812 31.0811 0.0511
    MPSP[27] 21.9251 0.1301 26.8977 0.0841 30.3806 0.0557
    DWBMP[23] 21.8674 0.1335 26.9524 0.0836 30.3441 0.0552
    本文DLARBOMP 22.0072 0.1296 27.2606 0.0810 31.7642 0.0473
    下载: 导出CSV
  • [1] 金坚, 谷源涛, 梅顺良. 压缩采样技术及其应用[J]. 电子与信息学报, 2010, 32(2): 470–475. doi: 10.3724/SP.J.1146.2009.00497.

    JIN Jian, GU Yuantao, and MEI Shunliang. An introduction to compressive sampling and its applications[J]. Journal of Electronics & Information Technology, 2010, 32(2): 470–475. doi: 10.3724/SP.J.1146.2009.00497.
    [2] 尹建平, 王志军. 弹药学[M]. 北京: 北京理工大学出版社, 2014: 132–139.

    YIN Jianping and WANG Zhijun. Ammunition Theory[M]. Beijing: Beijing Institute of Technology Press, 2014: 132–139.
    [3] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582.
    [4] CANDES E J, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489–509. doi: 10.1109/TIT.2005.862083.
    [5] CANDÈS E J. Compressive sampling[C]. International Congress of Mathematicians, Madrid, Spain, 2006: 1433–1452.
    [6] HIRSCH L, GONZALEZ M G, and REY VEGA L. A comparative study of time domain compressed sensing techniques for optoacoustic imaging[J]. IEEE Latin America Transactions, 2022, 20(6): 1018–1024. doi: 10.1109/TLA.2022.9757745.
    [7] 杨凯. 基于压缩感知的信道估计技术的研究[D]. [硕士论文], 电子科技大学, 2018: 22–23.

    YANG Kai. Research on channel estimation based on compressed sensing[D]. [Master dissertation], University of Electronic Science and Technology of China, 2018: 22–23.
    [8] DAVE P and JOSHI A. Prediction based method for faster compressive sensing reconstruction using OMP[C]. The 2019 2nd IEEE Middle East and North Africa Communications Conference (MENACOMM), Manama, Bahrain, 2019: 1–4. doi: 10.1109/MENACOMM46666.2019.8988557.
    [9] MALLAT S G and ZHANG Zhifeng. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415. doi: 10.1109/78.258082.
    [10] TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666. doi: 10.1109/TIT.2007.909108.
    [11] DAI Wei and MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230–2249. doi: 10.1109/TIT.2009.2016006.
    [12] NEEDELL D and VERSHYNIN R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics, 2009, 9(3): 317–334. doi: 10.1007/s10208-008-9031-3.
    [13] NEEDELL D and TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301–321. doi: 10.1016/j.acha.2008.07.002.
    [14] CHATTERJEE S, SUNDMAN D, and SKOGLUND M. Look ahead orthogonal matching pursuit[C]. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011: 4024–4027. doi: 10.1109/ICASSP.2011.5947235.
    [15] 曾春艳. 匹配追踪的最佳原子选择策略和压缩感知盲稀疏度重建算法改进[D]. [博士论文], 华南理工大学, 2013.

    ZENG Chunyan. Improvements on optimal atom selection strategies of matching pursuit and blind sparsity reconstruction algorithms in compressed sensing[D]. [Ph. D. dissertation], South China University of Technology, 2013.
    [16] LI Yuanjun and CHEN Wendong. A correlation coefficient sparsity adaptive matching pursuit algorithm[J]. IEEE Signal Processing Letters, 2023, 30: 190–194. doi: 10.1109/LSP.2023.3252469.
    [17] WANG Linyu, YE Pengfei, and XIANG Jianhong. A modified algorithm based on smoothed L0 norm in compressive sensing signal reconstruction[C]. The 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018: 1812–1816. doi: 10.1109/ICIP.2018.8451799.
    [18] KILIÇ B, GÜNGÖR A, KALFA M, et al. Sensing matrix design for compressive sensing based direction of arrival estimation[C]. The 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, 2020: 1–4. doi: 10.1109/SIU49456.2020.9302073.
    [19] ZEHNG Baifu, ZENG Cao, LI Shidong, et al. Joint sparse recovery for signals of spark-level sparsity and MMV tail-2, 1 minimization[J]. IEEE Signal Processing Letters, 2021, 28: 1130–1134. doi: 10.1109/LSP.2021.3084517.
    [20] ZHONG Xudong, YIN Hao, HE Yuanzhi, et al. Joint downlink power and time-slot allocation for distributed satellite cluster network based on Pareto optimization[J]. IEEE Access, 2017, 5: 25081–25096. doi: 10.1109/ACCESS.2017.2767061.
    [21] CHEN S S, DONOHO D L, and SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Journal on Scientific Computing, 1998, 20(1): 33–61. doi: 10.1137/S1064827596304010.
    [22] ZHU Mingdong, LI Mingyu, GENG Zhen, et al. Dice coefficient matching-based sparsity adaptive matching pursuit algorithm for the digital predistortion model pruning[C]. The 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China, 2018: 1032–1035. doi: 10.1109/ICCT.2018.8599901.
    [23] 季策, 王金芝, 耿蓉. 基于Dice系数的弱选择回溯匹配追踪算法[J]. 东北大学学报:自然科学版, 2021, 42(2): 189–195. doi: 10.12068/j.issn.1005-3026.2021.02.006.

    JI Ce, WANG Jinzhi, and GENG Rong. Weak-selection backtracking matching pursuit algorithm based on dice coefficient[J]. Journal of Northeastern University:Natural Science, 2021, 42(2): 189–195. doi: 10.12068/j.issn.1005-3026.2021.02.006.
    [24] 刘素娟, 崔程凯, 郑丽丽, 等. 基于压缩感知的贪婪类重构算法原子识别策略综述[J]. 电子与信息学报, 2023, 45(1): 361–370. doi: 10.11999/JEIT211297.

    LIU Sujuan, CUI Chengkai, ZHENG Lili, et al. A review of atom recognition strategies for greedy class reconstruction algorithms based on compressed sensing[J]. Journal of Electronics & Information Technology, 2023, 45(1): 361–370. doi: 10.11999/JEIT211297.
    [25] 刘浩强, 赵洪博, 冯文权. 基于CS的正则化稀疏度变步长自适应匹配追踪算法[J]. 北京航空航天大学学报, 2017, 43(10): 2109–2117. doi: 10.13700/j.bh.1001-5965.2016.0830.

    LIU Haoqiang, ZHAO Hongbo, and FENG Wenquan. Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2109–2117. doi: 10.13700/j.bh.1001-5965.2016.0830.
    [26] BAIG A, MOINUDDIN A A, and KHAN E. PSNR of highest distortion region: An effective image quality assessment method[C]. 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON), Aligarh, India, 2019: 1–4. doi: 10.1109/UPCON47278.2019.8980171.
    [27] BLANCHARD J D and TANNER J. Performance comparisons of greedy algorithms in compressed sensing[J]. Numerical Linear Algebra with Applications, 2015, 22(2): 254–282. doi: 10.1002/nla.1948.
    [28] WU Menghang, WU Feiyun, YANG Kunde, et al. A multipath matching pursuit algorithm based on improved-inner product matching criterion[C]. 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Macau, China, 2020: 1–5. doi: 10.1109/ICSPCC50002.2020.9259501.
    [29] 焦李成, 杨淑媛, 刘芳, 等. 压缩感知回顾与展望[J]. 电子学报, 2011, 39(7): 1651–1662.

    JIAO Licheng, YANG Shuyuan, LIU Fang, et al. Development and prospect of compressive sensing[J]. Acta Electronica Sinica, 2011, 39(7): 1651–1662.
    [30] LIU Guangcan, ZHANG Zhao, LIU Qingshan, et al. Robust subspace clustering with compressed data[J]. IEEE Transactions on Image Processing, 2019, 28(10): 5161–5170. doi: 10.1109/TIP.2019.2917857.
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出版历程
  • 收稿日期:  2023-06-10
  • 修回日期:  2023-09-22
  • 网络出版日期:  2023-10-18
  • 刊出日期:  2024-04-24

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