混响背景下基于核密度估计的动目标检测
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摘要
针对混响背景中的动目标检测问题,将基阵接收数据经过波束形成与匹配滤波后的输出视作统计观测空间,基于背景和目标回波在该空间中的统计特性差异,采用非参量核密度函数估计方法构造多ping情况下的检验统计量,实现运动目标回波检测。理论计算获得不同信混比下的ROC曲线,与单ping波束形成及匹配滤波方法相比,在保证虚警概率小于0. 01,检测概率大于0. 5的条件下,最小可检测信混比约降低6 d B。波形数据仿真与海上实录数据检验均表明该方法的检测性能优于单ping检测器。
Considering the detection of a moving target in a reverberating background,this study regarded the output of received data after beamforming and matched filtering as a statistical observation space. Based on the statistical characteristic difference of reverberation and target echoes,kernel density estimation was used to build test statistics by multi-ping output,which can achieve echo detection of the moving target. Theoretical calculation was conducted to obtain ROC curves under different signal-reverberation ratio conditions. Compared with single-ping beamforming and matched filtering method,under the condition of ensuring the false alarm probability lower than 0. 01 and the detection probability higher than 0. 5,the minimum detectable signal-reverberation ratio was 6 dB less than that of the traditional method. Simulations and sea trial results show that the new method performs better than traditional detection using single ping.
Considering the detection of a moving target in a reverberating background,this study regarded the output of received data after beamforming and matched filtering as a statistical observation space. Based on the statistical characteristic difference of reverberation and target echoes,kernel density estimation was used to build test statistics by multi-ping output,which can achieve echo detection of the moving target. Theoretical calculation was conducted to obtain ROC curves under different signal-reverberation ratio conditions. Compared with single-ping beamforming and matched filtering method,under the condition of ensuring the false alarm probability lower than 0. 01 and the detection probability higher than 0. 5,the minimum detectable signal-reverberation ratio was 6 dB less than that of the traditional method. Simulations and sea trial results show that the new method performs better than traditional detection using single ping.
引文
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[9]LEE W J,STANTON T K.Statistics of echoes from mixed assemblages of scatterers with different scattering amplitudes and numerical densities[J].IEEE journal of oceanic engineering,2014,39(4):740-754.
[10]汪德昭,尚尔昌.水声学[M].2版.北京:科学出版社,1981.WANG Dezhao,SHANG Erchang.Underwater acoustics[M].2nd ed.Beijing:Science Press,1981.
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[15]奥里雪夫斯基B B.海洋混响的统计特性[M].罗耀杰,译.北京:科学出版社,1977:85-87.OLICHEWSKI B B.Statistical characteristics of marine reverberation[M].LUO Yaojie,trans.Beijing:Science Press,1977:85-87.
[2]REN Jinyun,BIRD J S.Detecting small slow-moving sonar targets using bottom reverberation[C]//OCEANS 2006.Boston,USA,2006:1-6.
[3]毛盾,刘忠,程远国.基于蛙人探测声呐序列图像的水下小目标检测算法[J].传感技术学报,2011,24(7):1027-1032.MAO Dun,LIU Zhong,CHENG Yuanguo.Underwater small target detection algorithm based on diver detection sonar image sequences[J].Chinese journal of sensors and actuators,2011,24(7):1027-1032.
[4]魏长安,姜守达,孙超.基于核密度估计的前视红外小目标跟踪[J].哈尔滨工程大学学报,2009,30(7):763-767.WEI Chang'an,JIANG Shouchao,SUN Chao.Small target tracking in forward looking infrared imagery based on kernel density estimation[J].Journal of Harbin Engineering University,2009,30(7):763-737.
[5]ELGAMMAL A,DURAISWAMI R,HARWOOD D,et al.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J].Proceedings of the IEEE,2002,90(7):1151-1163.
[6]周恩策,刘纯平,张玲燕,等.基于时间窗的自适应核密度估计运动检测方法[J].通信学报,2011,32(3):106-114,124.ZhOU Ence,LIU Chunping,ZHANG Lingyan,et al.Foreground object detection based on time information window adaptive kernel density estimation[J].Journal on communications,2011,32(3):106-114,124.
[7]ABRAHAM D A,LYONS A P.Simulation of Non-Rayleigh reverberation and clutter[J].IEEE journal of oceanic engineering,2004,29(2):347-362.
[8]CHU Dezhang,STANTON T K.Statistics of echoes from a directional sonar beam insonifying finite numbers of single scatterers and patches of scatterers[J].IEEE journal of oceanic engineering,2010,35(2):267-277.
[9]LEE W J,STANTON T K.Statistics of echoes from mixed assemblages of scatterers with different scattering amplitudes and numerical densities[J].IEEE journal of oceanic engineering,2014,39(4):740-754.
[10]汪德昭,尚尔昌.水声学[M].2版.北京:科学出版社,1981.WANG Dezhao,SHANG Erchang.Underwater acoustics[M].2nd ed.Beijing:Science Press,1981.
[11]ABRAHAM D A.Signal excess in K-distributed reverberation[J].IEEE journal of oceanic engineering,2003,28(3):526-536.
[12]WAITE A D.Sonar for practising engineerings[M].3rd ed.Chichester:John Wiley&Sons,Ltd,2002.
[13]SCOTT D W.Multivariate density estimation[M].New York:Wiley-Interscience Publication,1992:125.
[14]SUHRE A,ARIKAN O,CETIN A E.Bandwidth selection for kernel density estimation using Fourier domain constraints[J].IET signal processing,2016,10(3):280-283.
[15]奥里雪夫斯基B B.海洋混响的统计特性[M].罗耀杰,译.北京:科学出版社,1977:85-87.OLICHEWSKI B B.Statistical characteristics of marine reverberation[M].LUO Yaojie,trans.Beijing:Science Press,1977:85-87.