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L0范数平滑逼近的稳健求解算法

王峰 向新 易克初 熊磊

王峰, 向新, 易克初, 熊磊. L0范数平滑逼近的稳健求解算法[J]. 电子与信息学报, 2015, 37(10): 2377-2382. doi: 10.11999/JEIT141590
引用本文: 王峰, 向新, 易克初, 熊磊. L0范数平滑逼近的稳健求解算法[J]. 电子与信息学报, 2015, 37(10): 2377-2382. doi: 10.11999/JEIT141590
Wang Feng, Xiang Xin, Yi Ke-chu, Xiong Lei. Robust Computational Methods for Smoothed L0 Approximation[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2377-2382. doi: 10.11999/JEIT141590
Citation: Wang Feng, Xiang Xin, Yi Ke-chu, Xiong Lei. Robust Computational Methods for Smoothed L0 Approximation[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2377-2382. doi: 10.11999/JEIT141590

L0范数平滑逼近的稳健求解算法

doi: 10.11999/JEIT141590
基金项目: 

国家自然科学基金(61379104)和陕西省自然科学基金(2014JM2- 6106)

Robust Computational Methods for Smoothed L0 Approximation

Funds: 

The National Natural Science Foundation of China (61379104)

  • 摘要: 该文研究基于代理函数和先验概率密度的L0范数平滑逼近问题的稳健求解。首先,分析了平滑逼近函数的凹凸特性,给出提高恢复性能的参数调整策略与改进的SL0和FOCUSS算法。其次,将噪声背景下L0范数逼近过程进行正则化表示,并基于牛顿方向推导其迭代重加权形式的求解框架,给出一种新的代理函数。最后,使用数值仿真证实了所提算法可以提高此类问题的求解的稳健性,具有实用价值。
  • Cands E J and Wakin M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
    Mohimani H, Zadeh M, and Jutten C. A fast approach for overcomplete sparse decomposition based on smoothed L0 norm[J]. IEEE Transactions on Signal Processing, 2009, 57(1): 289-301.
    Hyder M M and Mahata K. An improved smoothed L0 approximation algorithm for sparse representation[J]. IEEE Transactions on Signal Processing, 2010, 58(4): 2194-2205.
    Lv J, Huang L, Shi Y, et al.. Inverse synthetic aperture radar imaging via modified smoothed L0 norm[J]. IEEE Antennas and Wireless Propagation Letters, 2014, 13(7): 1235-1238.
    Liu Z, You P, Wei X, et al.. Dynamic ISAR imaging of maneuvering targets based on sequential SL0[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1041-1045.
    Guo L and Wen X. SAR image compression and reconstruction based on compressed sensing[J]. Journal of Information Computational Science, 2014, 11(2): 573-579.
    Liu Z, Wei X, and Li X. Aliasing-free micro-Doppler analysis based on short-time compressed sensing[J]. IET Signal Processing, 2013, 8(2): 176-187.
    Liu T and Zhou J. Improved smoothed L0 reconstruction algorithm for ISI sparse channel estimation[J]. The Journal of China Universities of Posts and Telecommunications, 2014, 21(2): 40-47.
    Ye X and Zhu W. Sparse channel estimation of pulse-shaping multiple-input-multiple-output orthogonal frequency division multiplexing systems with an approximate gradient L2-SL0 reconstruction algorithm[J]. IET Communications, 2014, 8(7): 1124-1131.
    王军华, 黄知涛, 周一宇, 等. 基于近似L0 范数的稳健稀疏重构算法[J]. 电子学报, 2012, 40(6): 1185-1189.
    Wang Jun-hua, Huang Zhi-tao, Zhou Yi-yu, et al.. Robust sparse recovery based on approximate L0 norm[J]. Acta Electronica Sinica, 2012, 40(6): 1185-1189.
    赵瑞珍, 林婉娟, 李浩, 等. 基于光滑L0范数和修正牛顿法的压缩感知重建算法[J]. 计算机辅助设计与图形学学报, 2012, 24(4): 478-484.
    Zhao Rui-zhen, Lin Wan-juan, Li Hao, et al.. Reconstruction algorithm for compressive sensing based on smoothed L0 norm and revised newton method[J]. Journal of Computer- Aided Design Computer Graphic, 2012, 24(4): 478-484.
    杨良龙, 赵生妹, 郑宝玉, 等. 基于SL0 压缩感知信号重建的改进算法[J]. 信号处理, 2012, 28(6): 834-841.
    Yang Liang-long, Zhao Sheng-mei, Zheng Bao-yu, et al.. The improved reconstruction algorithm for compressive sensing on SL0[J]. Signal Processing, 2012, 28(6): 834-841.
    余付平, 沈堤. 基于拟牛顿方向的改进平滑L0 算法[J]. 计算机工程与应用, 2013, 49(22): 215-218.
    Yu Fu-ping and Shen Di. Improved smoothed L0 approximation algorithm based on Quasi-Newton direction[J]. Computer Engineering and Applications, 2013, 49(22): 215-218.
    贺亚鹏, 庄珊娜, 张燕洪, 等. 一种基于交叉验证的稳健SL0目标参数提取算法[J]. 系统工程与电子技术, 2012, 34(1): 64-68.
    He Ya-peng, Zhuang Shan-na, Zhang Yan-hong, et al.. Cross validation based robust-SL0 algorithm for target parameter extraction[J]. Systems Engineering and Electronics, 2012, 34(1): 64-68.
    邱伟, 赵宏钟, 陈建军, 等. 基于平滑L0 范数的高分辨雷达一维成像研究[J]. 电子与信息学报, 2011, 33(12): 2869-2874.
    Qiu Wei, Zhao Hong-zhong, Chen Jian-jun, et al.. High- resolution radar one-dimensional imaging based on smoothed L0 norm[J]. Journal of Electronics Information Technology, 2011, 33(12): 2869-2874.
    Gorodnitsky I F and Rao B D. Sparse signal reconstruction from limited data using FOCUSS: a reweighted minimum norm algorithm[J]. IEEE Transactions on Signal Processing, 1997, 45(3): 600-616.
    Pant J K, Lu W, and Antoniou A. New improved algorithms for compressive sensing based on Lp norm[J]. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2014, 61(3): 198-202.
    Yuille A L and Rangarajan A. The concave-convex procedure [J]. Neural Computer, 2003, 15(4): 915-936.
    Rao B D, Engan K, Cotter S F, et al.. Subset selection in noise based on diversity measure minimization[J]. IEEE Transactions on Signal Processing, 2003, 51(3): 760-770.
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出版历程
  • 收稿日期:  2014-12-11
  • 修回日期:  2015-06-03
  • 刊出日期:  2015-10-19

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