基于EMD算法的海杂波信号去噪
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摘要
为有效提取噪声背景下的海杂波信号,针对实际海杂波信号非线性非平稳的特点,提出基于EMD算法对实测海杂波数据去噪。对噪声水平未知条件下,EMD算法分解的哪些内蕴模式是信号部分难以有效界定的问题,提出基于噪声主要在高频段且能量较小、信号能量主要集中在低频段思想的噪声判断准则。为验证EMD去噪效果,将该算法对含有噪声的海杂波实测数据进行去噪,采用信噪比和均方差两项指标衡量去噪效果,并与均值、中值、db2小波等去噪方法对比,EMD算法在这两项指标均优于其他算法,说明EMD算法对海杂波数据去噪是有效的。
In order to effectively extract the sea clutter signal within noisy background,a signal-filtering method based on empirical mode decomposition(EMD) is presented for sea clutter denoising in consideration that the real sea clutter is nonlinear and nonstationary.As to the intrinsic mode functions from EMD which belong to the signal can not be determined when the noise-level is unknown,this paper presents a noise judge criterion based on the basic idea that the noise is concentrated on high frequency ones and contains little energy,while the energy of signal is mainly concentrated on the low frequency ones.The noisy real-life sea clutter dataset is used to test the validity of EMD denoising.The denoised signal-to-noise ratio(DSNR) and mean square error(MSE) are employed to measure the efficiency of noise reduction.The EMD filtering outperforms the averaging,median,and db2 wavelet methods,so the EMD-based denoising for real-life sea clutter is effective.
In order to effectively extract the sea clutter signal within noisy background,a signal-filtering method based on empirical mode decomposition(EMD) is presented for sea clutter denoising in consideration that the real sea clutter is nonlinear and nonstationary.As to the intrinsic mode functions from EMD which belong to the signal can not be determined when the noise-level is unknown,this paper presents a noise judge criterion based on the basic idea that the noise is concentrated on high frequency ones and contains little energy,while the energy of signal is mainly concentrated on the low frequency ones.The noisy real-life sea clutter dataset is used to test the validity of EMD denoising.The denoised signal-to-noise ratio(DSNR) and mean square error(MSE) are employed to measure the efficiency of noise reduction.The EMD filtering outperforms the averaging,median,and db2 wavelet methods,so the EMD-based denoising for real-life sea clutter is effective.
引文
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[2]林三虎,朱红,赵亦工.海杂波的混沌特性分析[J].系统工程与电子技术,2004,26(2):178-181.LIN San-hu,ZHU Hong,ZHAO Yi-gong.Study ofSea Clutter Dynamics[J].System Engineering andElectronics,2004,26(2):178-181.(in Chinese)
[3]石志广,周剑雄,赵宏钟,等.海杂波的多重分形特性分析[J].数据采集与处理,2006,21(2):168-173.SHI Zhi-guang,ZHOU Jian-xiong,ZHAO Hong-zhong,et al.Multifractal Analysis of Radar Sea Clut-ter[J].Journal of Data Acquisition&Processing,2006,21(2):168-173.(in Chinese)
[4]Borge J C N,Rodriguez G R,Hessner K.Inversion ofNautical Radar I mages for Surface Wave Analysis[J].Journal of At mospheric and Oceanic Technology,2004,21(8):1291-1300.
[5]王福友,袁赣南,卢志忠.X波段导航雷达浪高实时测量研究[J].海洋工程,2007,25(4):84-87.WANG Fu-you,YUAN Gan-nan,LUZhi-zhong.Re-search on Real-Ti me Wave Height Measurement UsingX-Band Navigation Radar[J].the Ocean Engineering,2007,25(4):84-87.(in Chinese)
[6]Haykin S,Puthusserypady S.Chaotic Dynamic of SeaClutter[J].Chaos,1997,7(4):777-802.
[7]Borge J C N,Reichert K.Use of Nautical Radar as aWave Monitoring Instrument[J].Coastal Engineer-ing,1999,37(3):331-342.
[8]Haykin S,Bakker R,Currie B W.Uncovering Non-linear Dynamics—the Case Study of Sea Clutter[J].Proceedings of the IEEE,2002,90(5):860-881.
[9]Jing H,Wenwen T,Jianbo G.Detection of Low Ob-servable Targets Within Sea Clutter by StructureFunction Based Multifractal Analysis[J].IEEE Trans on Antennas and Propaga-tion,2006,54(1):136-143.
[10]Guede S S C,Cherneva Z,Antao E M.Steepnessand Asymmetry of the Largest Waves in Storm SeaStates[J].Ocean Engineering,2004,31(8-9):1147-1167.
[11]刘瑞平,沈福民.混沌时间序列预测与目标检测[J].雷达科学与技术,2005,3(6):327-331.LI U Rui-ping,SHEN Fu-min.Chaos Ti me SeriesPrediction and Target Detection[J].Radar Scienceand Technology,2005,3(6):327-331.(in Chinese)
[12]Boudraa A O,Cexus J C,Salzenstein F,et al.EMD-Based Signal Noise Reduction[J].International Jour-nal of Signal Processing,2004,1(1):33-37.
[13]张俊,柳健.SAR图像斑点噪声的小波软门限滤除算法[J].测绘学报,1998,27(2):20-25.ZHANGJun,LI UJian.A Speckle Reduction Algo-rithm by Soft-Thresholding Based on Wavelet Filtersfor SARI mages[J].Acta Geodaetica et Cartographi-ca Sinica,1998,27(2):20-25.(in Chinese)
[14]刘喜武,刘洪,李幼铭,等.局域波分解及其在地震信号时频分析中的应用[J].地球物理学进展,2007,22(2):365-375.LI U Xi-wu,LI U Hong,LI You-ming,et al.LocalWaves Decomposition and Its Applications to SeismicSignal Ti me-Frequency Analysis[J].Progress in Ge-ophysics,2007,22(2):365-375.(in Chinese)
[15]Huang N E,Shen Z,Long S R,et al.The EmpiricalMode Decomposition and the Hilbert Spectrum forNonlinear Nonstationary Ti me Series Analysis[J].Proceedings of the Royal Society of London A,1998,454(1971):903-995.
[16]Flandrin P,Rilling G,Goncalves P.Empirical ModeDecomposition as a Filter Bank[J].IEEE Signal Pro-cessing Letters,2004,11(2):112-114.
[17]Haykin S.The Dart mouth Database of IPIX Radar[EB/OL].(2001-06-08)[2007-5-23]http://soma.ece.mcmaster.ca/ipix/Dart mouth/datasets.ht ml.
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