基于多普勒谱非广延熵的海面目标检测方法

陈世超 罗丰 胡冲 聂学雅

陈世超, 罗丰, 胡冲, 等. 基于多普勒谱非广延熵的海面目标检测方法[J]. 雷达学报, 2019, 8(3): 344–354. doi: 10.12000/JR19012
引用本文: 陈世超, 罗丰, 胡冲, 等. 基于多普勒谱非广延熵的海面目标检测方法[J]. 雷达学报, 2019, 8(3): 344–354. doi: 10.12000/JR19012
CHEN Shichao, LUO Feng, HU Chong, et al. Small target detection in sea clutter background based on Tsallis entropy of Doppler spectrum[J]. Journal of Radars, 2019, 8(3): 344–354. doi: 10.12000/JR19012
Citation: CHEN Shichao, LUO Feng, HU Chong, et al. Small target detection in sea clutter background based on Tsallis entropy of Doppler spectrum[J]. Journal of Radars, 2019, 8(3): 344–354. doi: 10.12000/JR19012

基于多普勒谱非广延熵的海面目标检测方法

doi: 10.12000/JR19012
基金项目: 国家重大科学仪器设备开发专项资金(2013YQ20060705)
详细信息
    作者简介:

    陈世超(1992–),女,西安电子科技大学雷达信号处理国家重点实验室博士生,研究方向为海杂波建模与仿真、海杂波背景下的目标检测。E-mail: scchen0115@163.com

    罗丰:罗   丰(1971–),男,西安电子科技大学雷达信号处理国家重点实验室博士生导师,教授,研究方向为雷达系统设计、雷达信号与信息处理、高速实时信号处理。E-mail: luofeng@xidian.edu.cn

    胡冲:胡   冲(1987–),男,西安电子科技大学雷达信号处理国家重点实验室博士生,现就职于西南电子技术研究所,研究方向为海杂波特性分析与建模。E-mail: hake_hc@163.com

    聂学雅(1995–),女,西安电子科技大学雷达信号处理国家重点实验室硕士生,研究方向为雷达目标检测。E-mail: cute_nie0019@163.com

    通讯作者:

    罗丰  luofeng@xidian.edu.cn

  • 中图分类号: TN959.72

Small Target Detection in Sea Clutter Background Based on Tsallis Entropy of Doppler Spectrum

Funds: The National Key Scientific Instrument and Equipment Development (2013YQ20060705)
More Information
  • 摘要: 根据海杂波和目标多普勒谱的聚集性差异,可以用熵特征来检测海杂波背景下的小目标,然而常用的香农熵仅仅是统计学角度的宏观量值,并不能反映出海杂波的非线性特性。非广延熵是香农熵的推广,可以描述海杂波已被证实的多重分形特性。该文首先给出了非广延熵与分形维数的关系,然后结合有目标单元回波的多普勒谱较纯杂波单元回波的多普勒谱聚集性更强以及海杂波回波具有多重分形特性的特点,提出了基于多普勒谱非广延熵的海杂波背景下的小目标检测方法,最后通过实测数据进行实验比较,验证了该文算法的有效性,在观测时间较短的情况下,与现有的多重分形频域Hurst指数方法和基于香农熵的方法相比,该文算法具有更好的检测性能。

     

  • 图  1  4种极化方式的平均信杂比

    Figure  1.  The average SCR of four polarizations

    图  2  目标单元${\log _2}\!\left( {{F^{(q)}}(m)} \right) \sim {\log _2}(m)$曲线

    Figure  2.  ${\log _2}\!\left( {{F^{(q)}}(m)} \right) \sim {\log _2}(m)$ Curve of target cell

    图  3  纯杂波单元${\log _2}\!\left( {{F^{(q)}}(m)} \right) \sim {\log _2}(m)$曲线

    Figure  3.  ${\log _2}\!\left( {{F^{(q)}}(m)} \right) \sim {\log _2}(m)$ Curve of clutter cell

    图  4  $H(q) \sim q$曲线

    Figure  4.  $H(q) \sim q$ Curve

    图  5  纯海杂波Doppler谱与含目标Doppler谱比较

    Figure  5.  Comparison of Doppler spectrums of target and pure clutter cell

    图  6  不同$q$时的归一化2维图

    Figure  6.  Normalized two-dimensional graphs of different $q$ values

    图  7  目标与杂波单元非广延熵统计直方图

    Figure  7.  Histogram of Tsallis entropy of target cell and clutter cells

    图  8  所提算法实现流程图

    Figure  8.  Flow chart of proposed algorithm

    表  1  1993年IPIX雷达数据主要参数说明

    Table  1.   Description of the data sets of IPIX radar database in 1993

    数据编号目标所在单元受目标影响单元风速(km/h)浪高(m)
    #1798:1192.2
    #2676:891.1
    #3076:8190.9
    #3176:9190.9
    #4075:891.0
    #5487:10200.7
    #28087:10101.6
    #31076:9330.9
    #31176:9330.9
    #32076:9280.9
    下载: 导出CSV

    表  2  全部单元多普勒谱非广延熵值

    Table  2.   Tsallis entropy of Doppler spectrum of all the cells

    距离单元号$q = 0.5$$q = 1.0$$q = 2.0$$q = 3.0$$q = 4.0$$q = 5.0$
    10.79920.89190.93870.95640.96740.9748
    20.79660.88510.93550.95530.96720.9751
    30.79940.87680.93110.95330.96620.9744
    40.81470.87570.92280.94490.95910.9687
    50.82160.87270.91670.93920.95420.9647
    60.81770.87070.91800.94180.95710.9674
    70.67050.68340.71650.75110.78050.8045
    80.60250.60170.63300.67250.70640.7343
    90.64290.65220.68270.71910.75050.7760
    100.74580.78390.82240.85260.87600.8939
    110.80800.86680.91230.93680.95300.9642
    120.81520.87170.91530.93720.95190.9623
    130.80810.86350.91310.93820.95410.9647
    140.80430.86710.91770.94190.95700.9672
    下载: 导出CSV

    表  3  不同FFT点数下最佳q

    Table  3.   The best q values of different FFT points

    数据编号64128256512
    #280HH6332
    #280VV5321
    #280HV4322
    #310HH3212
    #310VV8333
    #310HV3221
    #311HH3211
    #311VV5321
    #311HV3211
    下载: 导出CSV

    表  4  不同$\text{q}$值下算法的检测概率(%)

    Table  4.   Detection probability of the proposed algorithm of different $\text{q}$ values (%)

    数据编号$q$取值${P_f}$
    ${10^{ - 3}}$ ${10^{ - 2}}$ ${10^{ - 1}}$
    HHVVHVHHVVHVHHVVHV
    #280q = 182.0990.1496.78 85.9292.5698.19 92.7695.37100
    q = 290.5491.9597.1893.5695.7798.7995.9898.79100
    q = 392.1593.5696.3894.1698.1998.3996.5899.40100
    q = 1082.4989.9489.5489.7495.7792.3596.5899.4098.99
    #310q = 194.1613.2692.1596.5830.3895.3798.1962.1797.79
    q = 295.9824.3594.1697.9950.9197.3898.7975.8698.39
    q = 392.5640.4492.7697.5954.5397.1898.5977.0699.40
    q = 1068.6142.4578.2792.1555.3385.5198.3972.0399.20
    #311q = 110098.59100100100100100100100
    q = 210099.20100100100100100100100
    q = 310098.59100100100100100100100
    q = 1096.7880.4897.5998.7980.9310010099.60100
    下载: 导出CSV

    表  5  3种算法的检测概率(%)

    Table  5.   Detection probability of the three algorithms (%)

    序列长度方法${P_{\rm f}}$
    ${10^{ - 3}}$${10^{ - 2}}$${10^{ - 1}}$
    27频域Hurst指数法1.0016.3046.20
    q = 1(香农熵)58.9769.5779.48
    q = 3(非广延熵)72.9478.4988.21
    28频域Hurst指数法8.5021.3035.80
    q = 1(香农熵)82.0985.9292.76
    q = 3(非广延熵)92.1594.1696.58
    29频域Hurst指数法15.5032.8043.10
    q = 1(香农熵)82.1684.2390.04
    q = 3(非广延熵)88.3895.4497.51
    210频域Hurst指数法52.9063.8078.90
    q = 1(香农熵)61.9580.5393.81
    q = 3(非广延熵)89.3898.23100
    下载: 导出CSV

    表  6  观测时间为0.064 s所提方法检测概率(%)

    Table  6.   Detection probability when observation time is 0.064 s (%)

    数据编号${10^{ - 3}}$${10^{ - 2}}$${10^{ - 1}}$
    #280HH35.5146.8361.53
    #280VV38.0747.4759.42
    #280HV54.6062.1274.13
    #310HH45.0152.2366.99
    #310VV9.6415.6930.99
    #310HV45.1554.0667.34
    #311HH82.2989.5295.52
    #311VV64.2474.0386.08
    #311HV85.9893.3197.15
    下载: 导出CSV

    表  7  观测时间为0.128 s所提方法检测概率(%)

    Table  7.   Detection probability when observation time is 0.128 s(%)

    数据编号${10^{ - 3}}$${10^{ - 2}}$${10^{ - 1}}$
    #280HH73.6478.4988.31
    #280VV72.1578.1085.73
    #280HV88.2192.3795.24
    #310HH80.5788.8095.24
    #310VV31.6238.7558.97
    #310HV83.2591.9797.42
    下载: 导出CSV

    表  8  观测时间为0.032 s所提方法检测概率(%)

    Table  8.   Detection probability when observation time is 0.032 s (%)

    数据编号${10^{ - 3}}$${10^{ - 2}}$${10^{ - 1}}$
    #311HH32.0840.5553.25
    #311VV15.5423.4534.94
    #311HV40.3648.3559.76
    下载: 导出CSV
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  • 收稿日期:  2019-01-24
  • 修回日期:  2019-02-09
  • 网络出版日期:  2019-06-01

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