高分辨率有源声呐强混响抑制技术研究
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摘要
有源声呐分辨率的提高可以抑制混响,但也使声呐包络数据的统计分布偏离瑞利分布,而更接近拖尾较重的K分布。强散射体所带来的强混响的幅值一般较大,它们使统计分布拖尾更严重,表征K分布的形状参数也越小。强混响作为目标干扰,严重影响了背景功率估计的准确性,从而降低了目标检测的性能。本文基于模糊统计理论,首先提出了用于抑制强混响的模糊统计归一化处理方法;然后对强混响和模糊统计归一化处理如何影响声呐数据分布和CFAR(Constant false-alarm rate)目标检测性能进行了仿真、研究和分析,最后对基于模糊统计归一化处理的CFAR检测性能和传统CFAR检测性能进行了仿真比较。仿真结果表明强混响目标干扰能使K分布数据的形状参数变小,而模糊统计归一化处理可抑制强混响目标干扰,增大包络数据分布的形状参数,提高形状参数估计性能和CFAR检测性能。
Resolution enhancement of active sonar can suppress the reverberation. While it also makes the envelop data distribution diverge from Rayleigh to K distribution. The stronger scattering speckles, the heavier of the K distribution tails. The envelope amplitudes of these strong scattering speckles are usually very big. As the interfering target, the strong reverberation decreases the performances of the background power level estimation and the target detection. The fuzzy statistical normalization processing(FSNP) is introduced to suppress the strong reverberation firstly in this paper.Then how the strong reverberation and the FSNP affect the distribution of K-distributed sonar data is studied. The influence on the Constant false-alarm rate(CFAR) detection performance caused by the strong reverberation and the FSNP is also simulated and analyzed. Performance comparisons between the CFAR detector based on FSNP and the conventional CFAR detectors are carried out. The simulation results show that the strong reverberation can make the shape parameter of the interfering K-distributed data become smaller than that of the original K-distributed data. While the FSNP can suppress the strong reverberation, increase the shape parameter value, and improve the performance of the shape parameter estimator and the CFAR detector.
Resolution enhancement of active sonar can suppress the reverberation. While it also makes the envelop data distribution diverge from Rayleigh to K distribution. The stronger scattering speckles, the heavier of the K distribution tails. The envelope amplitudes of these strong scattering speckles are usually very big. As the interfering target, the strong reverberation decreases the performances of the background power level estimation and the target detection. The fuzzy statistical normalization processing(FSNP) is introduced to suppress the strong reverberation firstly in this paper.Then how the strong reverberation and the FSNP affect the distribution of K-distributed sonar data is studied. The influence on the Constant false-alarm rate(CFAR) detection performance caused by the strong reverberation and the FSNP is also simulated and analyzed. Performance comparisons between the CFAR detector based on FSNP and the conventional CFAR detectors are carried out. The simulation results show that the strong reverberation can make the shape parameter of the interfering K-distributed data become smaller than that of the original K-distributed data. While the FSNP can suppress the strong reverberation, increase the shape parameter value, and improve the performance of the shape parameter estimator and the CFAR detector.
引文
1李风华,刘建军.浅海混响的垂直相干性.声学学报,2003;28(6):494-503
2许彦伟,张宝华,张春华等.非瑞利海洋混响抑制技术研究.声学学报,2012;37(5):489-494
3李维,马晓川,侯朝焕等.用于混响中信号检测的空时预白化处理器.声学学报,2010;35(1):53-58
4马晓川,彭方和,李璇等.信源空间散布条件下的声呐目标滤波方法.声学学报,2013;38(1):1-10
5詹昊可,蔡志明,苑秉成.鱼雷声呐空时自适应混响抑制方法.武汉理工大学学报(交通科学与工程版),2007;31(6):946-950
6郭国强,杨益新,孙超.前后混响零点约束下基于时反算子分解的信混比增强方法研究.声学学报,2008;33(2):117-123
7姚万军,蔡志明,卫红凯.浅海混响建模的声束跟踪理论.声学学报,2009;34(3):223-228
8张荣瀚,李琪.非线性内波存在下的浅海远程混响模型.声学学报,2013;38(5):548-554
9 Watts S.Radar detection prediction in K-distributed sea clutter and thermal noise.IEEE Transactions on Aerospace and Electronic Systems,1987;23(1):40-45
10 Hou X,Morinaga N.Detection performance in Kdistributed and correlated Rayleigh clutters.IEEE Transactions on Aerospace and Electronic Systems,1989;25(5):634-641
11 Conte E,Lops M,Ricci G.Asymptotically optimum radar detection in compound-gaussian clutter.IEEE Transactions on Aerospace and Electronic Systems,1995;31(2):617-625
12 Watts S.Cell-averaging CFAR gain in spatially correlated K-distributed sea clutter.IEE Proceedings-Radar,Sonar and Navigation,1996;143(5):321-327
13 Abraham D A,Anthony P L.Novel physical interpretations of K-distributed reverberation.IEEE Journal of Oceanic Engineering,2002;27(4):800-813
14 Conte E,Maio A D,Galdi C.Statistical analysis of real clutter at different range resolutions.IEEE Transactions on Aerospace and Electronic Systems,2004;40(3):903-918
15 Ollila E,Tyler D E,Koivunen V et al.Complex elliptically symmetric distributions:Survey,new results and applications.IEEE Transactions on Signal Processing,2012;60(11):5597-5625
16 Kim J,Bell R M.A Computationally efficient CFAR algorithm based on a goodness-of-fit test for piecewise homogeneous environments.IEEE Transactions on Aerospace and Electronic Systems,2013;49(3):1519-1535
17 Raghavan R S.A method for estimating parameters of Kdistributed radar clutter.IEEE Transactions on Aerospace and Electronic Systems,1991;27(2):238-246
18 Iskander D R,Zoubir A M.Estimation of the parameters of the K-distribution using higher order and fractional moments.IEEE Transactions on Aerospace and Electronic Systems,1999;35(4):1453-1457
19 Blacknell D,Tough R J A.Parameter estimation for the Kdistribution based on[zlog(z)].IEE Proceedings-Radar,Sonar and Navigation,2001;148(6):309-312
20 Joughin I R,Percival D B,Winebrenner D P.Maximum likelihood estimation of K distribution parameters for SAR data.IEEE Transactions on Geroscience and Remote Sensing,1993;31(5):989-999
21 Roberts J J W,Furui S.Maximum likelihood estimation of Kdistribution parameters via the expectationmaximization algorithm.IEEE Transactions on Signal Processing,2000;48(12):3303-3306
22 Mezache A,Soltani F.A new approach for estimating the parameters of the K-distribution using fuzzy-neural networks.IEEE Transactions on Signal Processing,2008;56(11):5724-5728
23 Dong Y,Crisp D.Comparison of estimation schemes for the K-distribution shape parameter.In:Proceedings of International Radar Conference,Bordeaux,France,2009
24 Blacknell D.Comparison of parameter estimators for Kdistribution.IEE Proceedings-Radar,Sonar and Navigation,1994;141(1):45-52
25 Abraham D A,Lyons A P.Reliable methods for estimating the distribution shape parameter.IEEE Journal of Oceanic Engineering,2010;35(2):288-302
26 Xu Y,Hou C,Yan S et al.Fuzzy statistical normalization CFAR detector for non-Rayleigh data.IEEE Transactions on Aerospace and Electronic Systems,2015;51(1):383-396
27 Barnard T J,Khan F.Statistical normalization of spherically invariant non-Gaussian clutter.IEEE Journal of Oceanic Engineering,2004;29(2):303-309
28 Abraham D A.Statistical normalization of non-Rayleigh reverberation.In:Proc.MTS/IEEE OCEANS'97 Conference,1997:500-505
29 Swingler D N.On the application of the jackknife to the estimation of the parameters of short multisinusoidal signals using the data-matrix formulation.IEEE Transactions on Signal Processing,1995;43(9):2135-2143
30 Han K,Nehorai A.Improved source number detection and direction estimation with nested arrays and ULAs using Jackknifing.IEEE Transactions on Signal Processing,2013;61(23):6118-6128
31 Efron B.Bootstrap methods:another look at the Jackknife.The Annals of Statistics,1979;7(1):1-26
32 Lei S,Smith M R.Evaluation of several nonparametric bootstrap methods to estimate confidence intervals for software metrics.IEEE Transactions on software engineering,2003;29(11):996-1004
33 Pew-Thian Y,Hongyu A,Yasheng C et al.Uncertainty estimation in diffusion MRI using the nonlocal bootstrap.IEEE Transactions on medical imaging,2014;33(8):1627-1640
34 Finn H M,Johnson R S.Adaptive detection mode with threshold control as a function of spatially sampled clutter level estimate.RCA Review,1968;29(3):414-464
35 Hansen G V,Sawyers J H.Detectability loss due to greatest-of selection in a cell averaging CFAR.IEEE Transactions on Aerospace and Electronic Systems,1980;16(1):115-118
36 Trunk G V.Range resolution of targets using automatic detectors.IEEE Transactions on Aerospace and Electronic Systems,1978;14(5):750-755
37 Rohling H.Radar CFAR thresholding in clutter and multiple target situations.IEEE Transactions on Aerospace and Electronic Systems,1983;19(4):608-621
38 Gandhi P P,Kassam S A.Analysis of CFAR processors in nonhomogeneous background.IEEE Transactions on Aerospace and Electronic Systems,1988;24(4):427-445
39 Minett J W,Leung S W,Siu Y M et al.Estimating the length of a radar shadow in shadow-feature-enhanced detection using a fuzzy system.In:Proceedings of 10th IEEE International Conference on Fuzzy Systems,Melbourne,Australia,2001:115-118
40 Newhall B K.A physical model for the distribution of sonar clutter from a rough interface.In:Proceedings of the IEEE OCEANS Conference,Boston,MA,2006:1-4
2许彦伟,张宝华,张春华等.非瑞利海洋混响抑制技术研究.声学学报,2012;37(5):489-494
3李维,马晓川,侯朝焕等.用于混响中信号检测的空时预白化处理器.声学学报,2010;35(1):53-58
4马晓川,彭方和,李璇等.信源空间散布条件下的声呐目标滤波方法.声学学报,2013;38(1):1-10
5詹昊可,蔡志明,苑秉成.鱼雷声呐空时自适应混响抑制方法.武汉理工大学学报(交通科学与工程版),2007;31(6):946-950
6郭国强,杨益新,孙超.前后混响零点约束下基于时反算子分解的信混比增强方法研究.声学学报,2008;33(2):117-123
7姚万军,蔡志明,卫红凯.浅海混响建模的声束跟踪理论.声学学报,2009;34(3):223-228
8张荣瀚,李琪.非线性内波存在下的浅海远程混响模型.声学学报,2013;38(5):548-554
9 Watts S.Radar detection prediction in K-distributed sea clutter and thermal noise.IEEE Transactions on Aerospace and Electronic Systems,1987;23(1):40-45
10 Hou X,Morinaga N.Detection performance in Kdistributed and correlated Rayleigh clutters.IEEE Transactions on Aerospace and Electronic Systems,1989;25(5):634-641
11 Conte E,Lops M,Ricci G.Asymptotically optimum radar detection in compound-gaussian clutter.IEEE Transactions on Aerospace and Electronic Systems,1995;31(2):617-625
12 Watts S.Cell-averaging CFAR gain in spatially correlated K-distributed sea clutter.IEE Proceedings-Radar,Sonar and Navigation,1996;143(5):321-327
13 Abraham D A,Anthony P L.Novel physical interpretations of K-distributed reverberation.IEEE Journal of Oceanic Engineering,2002;27(4):800-813
14 Conte E,Maio A D,Galdi C.Statistical analysis of real clutter at different range resolutions.IEEE Transactions on Aerospace and Electronic Systems,2004;40(3):903-918
15 Ollila E,Tyler D E,Koivunen V et al.Complex elliptically symmetric distributions:Survey,new results and applications.IEEE Transactions on Signal Processing,2012;60(11):5597-5625
16 Kim J,Bell R M.A Computationally efficient CFAR algorithm based on a goodness-of-fit test for piecewise homogeneous environments.IEEE Transactions on Aerospace and Electronic Systems,2013;49(3):1519-1535
17 Raghavan R S.A method for estimating parameters of Kdistributed radar clutter.IEEE Transactions on Aerospace and Electronic Systems,1991;27(2):238-246
18 Iskander D R,Zoubir A M.Estimation of the parameters of the K-distribution using higher order and fractional moments.IEEE Transactions on Aerospace and Electronic Systems,1999;35(4):1453-1457
19 Blacknell D,Tough R J A.Parameter estimation for the Kdistribution based on[zlog(z)].IEE Proceedings-Radar,Sonar and Navigation,2001;148(6):309-312
20 Joughin I R,Percival D B,Winebrenner D P.Maximum likelihood estimation of K distribution parameters for SAR data.IEEE Transactions on Geroscience and Remote Sensing,1993;31(5):989-999
21 Roberts J J W,Furui S.Maximum likelihood estimation of Kdistribution parameters via the expectationmaximization algorithm.IEEE Transactions on Signal Processing,2000;48(12):3303-3306
22 Mezache A,Soltani F.A new approach for estimating the parameters of the K-distribution using fuzzy-neural networks.IEEE Transactions on Signal Processing,2008;56(11):5724-5728
23 Dong Y,Crisp D.Comparison of estimation schemes for the K-distribution shape parameter.In:Proceedings of International Radar Conference,Bordeaux,France,2009
24 Blacknell D.Comparison of parameter estimators for Kdistribution.IEE Proceedings-Radar,Sonar and Navigation,1994;141(1):45-52
25 Abraham D A,Lyons A P.Reliable methods for estimating the distribution shape parameter.IEEE Journal of Oceanic Engineering,2010;35(2):288-302
26 Xu Y,Hou C,Yan S et al.Fuzzy statistical normalization CFAR detector for non-Rayleigh data.IEEE Transactions on Aerospace and Electronic Systems,2015;51(1):383-396
27 Barnard T J,Khan F.Statistical normalization of spherically invariant non-Gaussian clutter.IEEE Journal of Oceanic Engineering,2004;29(2):303-309
28 Abraham D A.Statistical normalization of non-Rayleigh reverberation.In:Proc.MTS/IEEE OCEANS'97 Conference,1997:500-505
29 Swingler D N.On the application of the jackknife to the estimation of the parameters of short multisinusoidal signals using the data-matrix formulation.IEEE Transactions on Signal Processing,1995;43(9):2135-2143
30 Han K,Nehorai A.Improved source number detection and direction estimation with nested arrays and ULAs using Jackknifing.IEEE Transactions on Signal Processing,2013;61(23):6118-6128
31 Efron B.Bootstrap methods:another look at the Jackknife.The Annals of Statistics,1979;7(1):1-26
32 Lei S,Smith M R.Evaluation of several nonparametric bootstrap methods to estimate confidence intervals for software metrics.IEEE Transactions on software engineering,2003;29(11):996-1004
33 Pew-Thian Y,Hongyu A,Yasheng C et al.Uncertainty estimation in diffusion MRI using the nonlocal bootstrap.IEEE Transactions on medical imaging,2014;33(8):1627-1640
34 Finn H M,Johnson R S.Adaptive detection mode with threshold control as a function of spatially sampled clutter level estimate.RCA Review,1968;29(3):414-464
35 Hansen G V,Sawyers J H.Detectability loss due to greatest-of selection in a cell averaging CFAR.IEEE Transactions on Aerospace and Electronic Systems,1980;16(1):115-118
36 Trunk G V.Range resolution of targets using automatic detectors.IEEE Transactions on Aerospace and Electronic Systems,1978;14(5):750-755
37 Rohling H.Radar CFAR thresholding in clutter and multiple target situations.IEEE Transactions on Aerospace and Electronic Systems,1983;19(4):608-621
38 Gandhi P P,Kassam S A.Analysis of CFAR processors in nonhomogeneous background.IEEE Transactions on Aerospace and Electronic Systems,1988;24(4):427-445
39 Minett J W,Leung S W,Siu Y M et al.Estimating the length of a radar shadow in shadow-feature-enhanced detection using a fuzzy system.In:Proceedings of 10th IEEE International Conference on Fuzzy Systems,Melbourne,Australia,2001:115-118
40 Newhall B K.A physical model for the distribution of sonar clutter from a rough interface.In:Proceedings of the IEEE OCEANS Conference,Boston,MA,2006:1-4