基于局部Hilbert边际谱隶属度的微弱目标检测算法
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摘要
文章采用Hilbert-Huang变换处理海杂波数据,获得了海杂波的Hilbert边际谱,并分析了海杂波的Hilbert边际谱特点以及目标的影响。分析发现:无目标时,海杂波信号的Hilbert边际谱的谱宽较宽,低频成份较弱;而当目标出现时,其谱宽变窄,低频成份明显增强。为了描述目标对海杂波Hilbert边际谱的这种影响,将隶属度引入Hilbert边际谱,并采用目标出现频率处的海杂波局部Hilbert边际谱计算隶属度,通过分析发现,目标出现时,其隶属度明显减小,在此基础上,提出了基于局部Hilbert边际谱隶属度的微弱目标检测算法。仿真结果表明,对于实测和仿真目标数据,该算法的检测性能较好,明显优于基于盒维数的微弱目标检测算法、频域检测算法和多脉冲CA-CFAR检测算法的检测性能,对海杂波中的慢起伏固定目标具备较强的检测能力。
The sea clutter data is processed by the Hilbert-Huang transformation to obtain the Hilbert marginal spectrum of the sea clutter,and the characteristic of its spectrum and the effect of the target on the spectrum are analyzed.It is found that the spectrum of the sea clutter is wider and the low frequency components are weaker when no target is hit,while it is narrower and the low frequency components are stronger when the target is hit.To describe the effect of the target on the spectrum,the membership degree is introduced, which is calculated by the partial Hilbert marginal spectrum where the target is hit,and it is found that the membership degree is smaller obviously when the target is hit,so the method based on the membership degree of the partial Hilbert marginal spectrum is proposed to detect the weak target.Shown as the simulation results of the real and simulating target data,the slowly fluctuant fixed target in the sea clutter is detected effectively by the method whose detection performance is better than the method directly using the box dimension,the method to detect the target in the frequency domain and the multi-pulses CA-CFAR method.
The sea clutter data is processed by the Hilbert-Huang transformation to obtain the Hilbert marginal spectrum of the sea clutter,and the characteristic of its spectrum and the effect of the target on the spectrum are analyzed.It is found that the spectrum of the sea clutter is wider and the low frequency components are weaker when no target is hit,while it is narrower and the low frequency components are stronger when the target is hit.To describe the effect of the target on the spectrum,the membership degree is introduced, which is calculated by the partial Hilbert marginal spectrum where the target is hit,and it is found that the membership degree is smaller obviously when the target is hit,so the method based on the membership degree of the partial Hilbert marginal spectrum is proposed to detect the weak target.Shown as the simulation results of the real and simulating target data,the slowly fluctuant fixed target in the sea clutter is detected effectively by the method whose detection performance is better than the method directly using the box dimension,the method to detect the target in the frequency domain and the multi-pulses CA-CFAR method.
引文
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[17]W.Yanling,Z.Qi,L.Shitu,Classification method of scene pictures based on blur pattern recognition,Microcomputer Information,2007,vol.23,(34),pp,226-227.
[2]张晓利,关键,董云龙,何友.K分布杂波中距离扩展目标的Wald检测[J],信号处理,2010,26(9):1349- 1354. Zhang Xiaoli,Guanjian,Dong Yunlong.He You.Wald test of range-distributed targets in K-distributed clutter [J],Signal processing,,2010,26(9):1349-1354.(in Chinese)
[3]Li Xiaofei,Xu Xiaojian.A Statistical Model for Correlated K-distributed Sea Clutter[C],Congress on Image and Signal Processing,2008:408-412.
[4]简涛,何友,苏峰.非高斯杂波下修正的SDD_GLRI距离扩展目标检测器[J],电子学报,2009,37(12): 2662-2667. Jian Tao,He You,SU Feng,QU Chang-wen.Modified SDD-GLRT Detector for Range-Spread Target s in NonGaussian Clutter[J],acta electronica sinica,2009,37 (12):2662-2667.(in Chinese)
[5]王谷,方宁,苗俊刚.复杂目标角闪烁序列的混沌特性[J],电子学报,2009,37(7):1505-1508. Wang Gu,Fang Ning,Miao Jungang.The Chaos Feature of Glint Series for Complex Targets[J],acta electronica sinica,2009,37(7):1505-1508.(in Chinese)
[6]S.Haykin.Adaptive Radar Signal Processing[J].John Wiley & Sons,New York,2007 Chapter 3.
[7]LANCE M.KAPLAN.Improved SAR Target Detection via Extended Fractal Features[J],IEEE Trans.AES,2001, 37(2):436-450.
[8]刘亚波,李亚超,邢孟道,保铮.基于锥形尺度变换的弱小舰船目标检测[J],电子与信息学报,2009,31 (11):2575-2580. Liu Yabo,Li Yachao,Xing Mengdao,Bao Zheng,Detection of Weak Ship Target Based on Taper Scale Transform [J],Journal of Electronics & Information Technology, 2009,31(11):2575-2580.(in Chinese)
[9]黄小红,贺夏,辛玉林.基于时频特征的低分辨雷达微动多目标分辨方法[J],电子与信息学报,2010,32 (10):2342-2347. Huang Xiaohong,He Xia,Xin Yulin.Resolving Multiple Targets with Micro-motions Based on Time-frequency feature with Low-resolution Radar[J],Journal of Electronics & Information Technology,2010,32(10):2342-2347. (in Chinese)
[10]Norden E.Huang,Zheng Shen,Steven R.Long,etc. the empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [M].Proc.R.Soc.Lond.A,1998:903-995.
[11]徐振华,景占荣,贺宏洲,李纪华.Hilbert-Huang变换在非协作无源定位频率估计中的应用[J].陕西科技大学学报,2008,26(6):116-118. Xu Zhenhua,Jing Zhanrong,He Hongzhou,Li Jihua.Application of Hilbert-Huang Transform in Frequency Estimation of Passive Location[J].Journal of Shanxi University of Science and Technology(Natural 'Science Edition), 2008,26(6):116-118.(in Chinese)
[12]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].科学出版社,北京,2006:75-76.
[13]公茂盛,谢礼立.HHT方法在地震工程中的应用之初步探讨,世界地震工程,2003,19(3):39-43. Gong Maosheng,Xie Lili.Discussion on the application of HHT method to earthquake engineering[J].WORLD EARTHQUAKE ENGINEERING,2003,19(3):39-43. (in Chinese)
[14]A.J.McDonald,A.J.G.Baumgaertner,G.J.Fraser, S.E.George,and S.Marsh.Empirical Mode Decomposition of the atmospheric wave field[J].Ann.Geophys, 2007,3:375-383.
[15]http://soma.ece.mcmaster.ca/ipix[EB/OL].
[16]何友,关键,彭应宁.雷达自动检测与恒虚警处理[M].北京:清华大学出版社,1999.5:25-37.
[17]W.Yanling,Z.Qi,L.Shitu,Classification method of scene pictures based on blur pattern recognition,Microcomputer Information,2007,vol.23,(34),pp,226-227.
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