搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于小波leaders的海杂波时变奇异谱分布分析

熊刚 张淑宁 赵慧昌

引用本文:
Citation:

基于小波leaders的海杂波时变奇异谱分布分析

熊刚, 张淑宁, 赵慧昌

Time-varying singularity spectrum distribution of sea clutter based on wavelet leaders

Xiong Gang, Zhang Shu-Ning, Zhao Hui-Chang
PDF
导出引用
  • 海杂波的奇异谱分析不仅能从理论上揭示海洋表面的动力学机理,同时也是对海探测雷达的关键技术之一. 本文提出基于小波leaders的海杂波时变奇异谱分析方法,将时间信息引入海杂波的奇异谱分析之中,从而实现动态的解析描述海杂波随时间变化的奇异谱特性. 在理论上,通过信号自身加窗,将时间信息引入传统的奇异谱(或称多重分形谱),实现了对海杂波时变奇异谱分布分析;在算法上,充分利用了小波leaders技术对于多种奇异性的提取能力(包括chirp奇异性和cusp奇异性),通过对时变奇异性指数和时变尺度函数的Legendre变换,实现对海杂波时变奇异谱分布的计算;在应用部分,采用经典的多重分形模型随机小波序列(RWC)以及三级海态条件下连续波多普勒体制雷达海杂波进行仿真分析,实验结果表明:1)基于小波leaders的奇异谱分布能跟踪海杂波的时变尺度特性,有效展示其时变奇异性谱分布;2)算法具有较好的负矩特性和统计收敛性. 该方法能为复杂非线性系统及随机多重分形信号分析提供参考.
    Singularity spectrum analysis of sea clutter is the key technology of detecting radar for sea target, which can discover the dynamic mechanism of the sea surface theoretically. In this paper, based on wavelet leaders the time-varying singularity spectrum distribution of sea clutters is proposed, which introduces time information to the traditional singularity spectrum, and displays the time-varying characteristic of singularity spectrum analytically. In theory, by way of self-windowed fractal signal, we introduce the time information to the traditional singularity spectrum, and realize multifractal spectrum distribution of sea clutters. In algorithm, based on the wavelet leaders, we adapt the process of embodying chirp-type and cusp-type singularities, and obtain the time-varying singularity spectrum distribution of sea clutters by the Legendre transform of the time-varying scaling function. In practice, we analyze the classical multifractal modelrandom wavelet series and the real sea clutter data of continuous wave Doppler radar in level III sea state. Experiments indicate that (1) the time-varying singularity spectrum distribution based on wavelet leaders can trace the time-varying scale characteristic and display the time-varying singularity spectrum distribution of sea clutters; (2) the algorithm possesses good statistical convergence, low computational cost, and passive moment property. The time-varying singularity spectrum distribution based on wavelet leaders may serve as a reference sample for nonlinear dynamics and multifractal signal processing.
    • 基金项目: 国家自然科学基金(批准号:61171168,61301216,60702016)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61171168, 61301216, 60702016).
    [1]

    Aanstassopoulos V, Lampropolous G A 1995 IRC, Washington, DC, p662

    [2]
    [3]
    [4]

    Mandelbrot B B 1986 Nonlinear Dynamics, Nijhof, Dordrecht p279

    [5]
    [6]

    Grassberger P, Procaccia I 1983 Phys. Rev. Lett. 50 346

    [7]

    Frisch U, Sulem P L 1984 Phys. Fluids 27 1921

    [8]
    [9]

    Halsey T C, Jensen M H, Procaccia K P I 1986 Phys. Rev. A 33 1141

    [10]
    [11]

    Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, Havlin S, Bunde A, Stanley H E 2002 Physica A 316 87

    [12]
    [13]
    [14]

    Gu G F, Zhou W X 2006 Physical Rev. E 74 061104

    [15]
    [16]

    Chhabra A, Jensen R 1989 Phys. Rev. Lett. 62 121327

    [17]

    Gu G F, Zhou W X 2010 Phys. Rev. E. 82 011136

    [18]
    [19]
    [20]

    Zhou W X 2008 Physical Rev. E 77 066211

    [21]
    [22]

    Muzy J F, Bacry E, Arneodo A 1991 Phys. Rev. Lett. 67 3515

    [23]
    [24]

    Bacry E, Arneodo A, Muzy J F 1993 J. Stat. Phys. 70 635

    [25]

    Muzy J F, Bacry E, Arneodo A 1993 Phys. Rev. E 47 875

    [26]
    [27]
    [28]

    Arneodo A, Argoul A, Muzy J F, Bacry E 1995 Fractals 1 629

    [29]
    [30]

    Lashermes B, Jaffard S, Abry P 2005 ICASS, Philadelphia, USA 2005 pp161-164

    [31]

    Xiong G, Yang X N, Zhao H C 2008 ICIC, Shanghai CCIS 15 pp541-548

    [32]
    [33]
    [34]

    Arneodo A, Bacry E, Muzy J F. J 1998 Math. Phys. 39 4142

    [35]
    [36]

    Aubry J M, Jaffard S 2002 Commun. Math. Phys. 227 483

    [37]
    [38]

    Xiong G, Yang X N, Zhao H C 2005 IEEE MAPE, Beijing, 8 pp1236-1239

    [39]
    [40]

    Liu N B, Guan J, Huang Y, Wang G Q, He Y 2012 Acta Phys. Sin. 61 190503 (in Chinese) [刘宁波, 关键, 黄勇, 王国庆, 何友 2012 物理学报 61 190503]

    [41]

    Xing H Y, Gong P, Xu W 2012 Acta Phys. Sin. 61 160504 (in Chinese) [行鸿彦, 龚平, 徐伟 2012 物理学报 61 160504]

    [42]
    [43]

    W He J B, Liu Z, Hu S L 2011 Acta Phys. Sin. 60 110208 (in Chinese) [贺静波, 刘忠, 胡生亮 2011 物理学报 60 110208]

    [44]
    [45]

    Yang J, Bian B M, Yan Z G, Wang C Y, Li Z H 2011 Acta Phys. Sin. 60 100506 (in Chinese) [杨娟, 卞保民, 闫振纲, 王春勇, 李振华 2011 物理学报 60 100506]

    [46]
    [47]

    Xiong G, Zhang S N, Liu Q 2012 Physica A 391 4727

    [48]
    [49]

    p231 (in Chinese) [Falconer K 2007分形几何: 数学基础及其应用(第2版) (北京: 人民邮电出版社)第231页]

    [50]

    Falconer K 2007 Fractal Geometry: Mathematical Foundations and Applications (2nd Ed.) (Beijing: Posts & Telecom Press)

  • [1]

    Aanstassopoulos V, Lampropolous G A 1995 IRC, Washington, DC, p662

    [2]
    [3]
    [4]

    Mandelbrot B B 1986 Nonlinear Dynamics, Nijhof, Dordrecht p279

    [5]
    [6]

    Grassberger P, Procaccia I 1983 Phys. Rev. Lett. 50 346

    [7]

    Frisch U, Sulem P L 1984 Phys. Fluids 27 1921

    [8]
    [9]

    Halsey T C, Jensen M H, Procaccia K P I 1986 Phys. Rev. A 33 1141

    [10]
    [11]

    Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, Havlin S, Bunde A, Stanley H E 2002 Physica A 316 87

    [12]
    [13]
    [14]

    Gu G F, Zhou W X 2006 Physical Rev. E 74 061104

    [15]
    [16]

    Chhabra A, Jensen R 1989 Phys. Rev. Lett. 62 121327

    [17]

    Gu G F, Zhou W X 2010 Phys. Rev. E. 82 011136

    [18]
    [19]
    [20]

    Zhou W X 2008 Physical Rev. E 77 066211

    [21]
    [22]

    Muzy J F, Bacry E, Arneodo A 1991 Phys. Rev. Lett. 67 3515

    [23]
    [24]

    Bacry E, Arneodo A, Muzy J F 1993 J. Stat. Phys. 70 635

    [25]

    Muzy J F, Bacry E, Arneodo A 1993 Phys. Rev. E 47 875

    [26]
    [27]
    [28]

    Arneodo A, Argoul A, Muzy J F, Bacry E 1995 Fractals 1 629

    [29]
    [30]

    Lashermes B, Jaffard S, Abry P 2005 ICASS, Philadelphia, USA 2005 pp161-164

    [31]

    Xiong G, Yang X N, Zhao H C 2008 ICIC, Shanghai CCIS 15 pp541-548

    [32]
    [33]
    [34]

    Arneodo A, Bacry E, Muzy J F. J 1998 Math. Phys. 39 4142

    [35]
    [36]

    Aubry J M, Jaffard S 2002 Commun. Math. Phys. 227 483

    [37]
    [38]

    Xiong G, Yang X N, Zhao H C 2005 IEEE MAPE, Beijing, 8 pp1236-1239

    [39]
    [40]

    Liu N B, Guan J, Huang Y, Wang G Q, He Y 2012 Acta Phys. Sin. 61 190503 (in Chinese) [刘宁波, 关键, 黄勇, 王国庆, 何友 2012 物理学报 61 190503]

    [41]

    Xing H Y, Gong P, Xu W 2012 Acta Phys. Sin. 61 160504 (in Chinese) [行鸿彦, 龚平, 徐伟 2012 物理学报 61 160504]

    [42]
    [43]

    W He J B, Liu Z, Hu S L 2011 Acta Phys. Sin. 60 110208 (in Chinese) [贺静波, 刘忠, 胡生亮 2011 物理学报 60 110208]

    [44]
    [45]

    Yang J, Bian B M, Yan Z G, Wang C Y, Li Z H 2011 Acta Phys. Sin. 60 100506 (in Chinese) [杨娟, 卞保民, 闫振纲, 王春勇, 李振华 2011 物理学报 60 100506]

    [46]
    [47]

    Xiong G, Zhang S N, Liu Q 2012 Physica A 391 4727

    [48]
    [49]

    p231 (in Chinese) [Falconer K 2007分形几何: 数学基础及其应用(第2版) (北京: 人民邮电出版社)第231页]

    [50]

    Falconer K 2007 Fractal Geometry: Mathematical Foundations and Applications (2nd Ed.) (Beijing: Posts & Telecom Press)

  • [1] 张金鹏, 张玉石, 李清亮, 吴家骥. 基于不同散射机制特征的海杂波时变多普勒谱模型. 物理学报, 2018, 67(3): 034101. doi: 10.7498/aps.67.20171612
    [2] 杜文辽, 陶建峰, 巩晓赟, 贡亮, 刘成良. 基于双树复小波变换的非平稳时间序列去趋势波动分析方法. 物理学报, 2016, 65(9): 090502. doi: 10.7498/aps.65.090502
    [3] 奚彩萍, 张淑宁, 熊刚, 赵惠昌. 多重分形降趋波动分析法和移动平均法的分形谱算法对比分析. 物理学报, 2015, 64(13): 136403. doi: 10.7498/aps.64.136403
    [4] 周洁, 杨双波. 周期受击陀螺系统随时间演化波函数的多重分形. 物理学报, 2015, 64(20): 200505. doi: 10.7498/aps.64.200505
    [5] 行鸿彦, 张强, 徐伟. 海杂波FRFT域的分形特征分析及小目标检测方法. 物理学报, 2015, 64(11): 110502. doi: 10.7498/aps.64.110502
    [6] 熊杰, 陈绍宽, 韦伟, 刘爽, 关伟. 基于多重分形去趋势波动分析法的交通流多重分形无标度区间自动识别方法. 物理学报, 2014, 63(20): 200504. doi: 10.7498/aps.63.200504
    [7] 行鸿彦, 朱清清, 徐伟. 一种混沌海杂波背景下的微弱信号检测方法. 物理学报, 2014, 63(10): 100505. doi: 10.7498/aps.63.100505
    [8] 行鸿彦, 祁峥东, 徐伟. 基于选择性支持向量机集成的海杂波背景中的微弱信号检测. 物理学报, 2012, 61(24): 240504. doi: 10.7498/aps.61.240504
    [9] 刘宁波, 关键, 黄勇, 王国庆, 何友. 海杂波的分段分数布朗运动模型. 物理学报, 2012, 61(19): 190503. doi: 10.7498/aps.61.190503
    [10] 行鸿彦, 龚平, 徐伟. 海杂波背景下小目标检测的分形方法. 物理学报, 2012, 61(16): 160504. doi: 10.7498/aps.61.160504
    [11] 贺静波, 刘忠, 胡生亮. 基于海杂波散射特性的微弱信号检测方法. 物理学报, 2011, 60(11): 110208. doi: 10.7498/aps.60.110208
    [12] 马千里, 卞春华, 王俊. 脑电信号的标度分析及其在睡眠状态区分中的应用. 物理学报, 2010, 59(7): 4480-4484. doi: 10.7498/aps.59.4480
    [13] 行鸿彦, 金天力. 基于对偶约束最小二乘支持向量机的混沌海杂波背景中的微弱信号检测. 物理学报, 2010, 59(1): 140-146. doi: 10.7498/aps.59.140
    [14] 罗世华, 曾九孙. 基于多分辨分析的高炉铁水含硅量波动多重分形辨识. 物理学报, 2009, 58(1): 150-157. doi: 10.7498/aps.58.150
    [15] 杨小冬, 宁新宝, 何爱军, 都思丹. 基于多尺度的人体ECG信号质量指数谱分析. 物理学报, 2008, 57(3): 1514-1521. doi: 10.7498/aps.57.1514
    [16] 苟学强, 张义军, 董万胜. 基于小波的雷暴强放电前地面电场的多重分形分析. 物理学报, 2006, 55(2): 957-961. doi: 10.7498/aps.55.957
    [17] 邵元智, 钟伟荣, 任 山, 蔡志苏, 龚 雷. 纳米团聚生长的多重分形谱. 物理学报, 2005, 54(7): 3290-3296. doi: 10.7498/aps.54.3290
    [18] 于会生, 孙霞, 罗守福, 王永瑞, 吴自勤. 非晶Ni-Cu-P合金化学沉积过程的多重分形谱研究. 物理学报, 2002, 51(5): 999-1003. doi: 10.7498/aps.51.999
    [19] 孙 霞, 熊 刚, 傅竹西, 吴自勤. ZnO薄膜原子力显微镜图像的多重分形谱. 物理学报, 2000, 49(5): 854-862. doi: 10.7498/aps.49.854
    [20] 王晓平, 周 翔, 何 钧, 廖良生, 吴自勤. 有机薄膜电致发光失效过程的多重分形谱研究. 物理学报, 1999, 48(10): 1911-1916. doi: 10.7498/aps.48.1911
计量
  • 文章访问数:  4426
  • PDF下载量:  588
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-12-24
  • 修回日期:  2014-04-11
  • 刊出日期:  2014-08-05

/

返回文章
返回