混响背景下低秩矩阵恢复的目标亮点特征提取
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摘要
有源声呐探测水下目标时,混响干扰增加了从目标回波信号中提取目标亮点特征的难度.依据目标回波与混响在时频域上能量分布的相关性不同,采用自适应核时频分析方法将目标回波信号变换到时频域上进行分析.通过低秩矩阵恢复方法将目标回波与混响分到稀疏矩阵和低秩矩阵中,从而分离目标回波与混响,降低混响对回波信号的干扰.针对稀疏矩阵采用Hough变换提取回波中的亮点峰,得到目标的亮点特征。通过仿真和实验数据证明在较低信混比情况下通过低秩矩阵恢复方法能够在时频域上进一步区分目标回波与混响,达到抑制混响的目的,便于获取目标亮点特征。
When the active sonar detects the underwater target,the reverberation interference increases the difficulty of extracting the target bright spot feature from the target echo signal.According to the correlation between the target echo and the reverberation in the time-frequency domain,the adaptive-kernel function time-frequency analysis method is used to transform the target echo signal into the time-frequency domain.The target echo and reverberation are divided into the sparse matrix and the low rank matrix by the low rank matrix recovery method,thereby separating the target echo and the reverberation,and reducing the interference of the reverberation on the echo signal.The Hough transform is used to extract the bright peaks in the echoes for the sparse matrix,and the highlight features of the target are obtained.The simulation and actual experimental data prove that the low-rank matrix recovery method can further distinguish the target echo and reverberation in the time-frequency domain with low signal-to-mix ratio,so as to suppress reverberation and obtain the target bright spot features more easily.
When the active sonar detects the underwater target,the reverberation interference increases the difficulty of extracting the target bright spot feature from the target echo signal.According to the correlation between the target echo and the reverberation in the time-frequency domain,the adaptive-kernel function time-frequency analysis method is used to transform the target echo signal into the time-frequency domain.The target echo and reverberation are divided into the sparse matrix and the low rank matrix by the low rank matrix recovery method,thereby separating the target echo and the reverberation,and reducing the interference of the reverberation on the echo signal.The Hough transform is used to extract the bright peaks in the echoes for the sparse matrix,and the highlight features of the target are obtained.The simulation and actual experimental data prove that the low-rank matrix recovery method can further distinguish the target echo and reverberation in the time-frequency domain with low signal-to-mix ratio,so as to suppress reverberation and obtain the target bright spot features more easily.
引文
1高博,杨士莪,朴胜春,张海刚,莫亚枭.界面混响的耦合简正波理论研究.声学学报,2013; 38(5):517-522
2郭国强,杨益新,孙超,李博.浅海低频本地海底混响的波导不变性结构及抑制方法研究.声学学报,2009; 34(6):506-514
3汤渭霖.声呐目标回波的亮点模型.声学学报,1994; 19(2):92—100
4陈云飞,李桂娟,王振山等.水中目标回波亮点统计特征研究.物理学报,2013;62(8):084302
5朱广平,孙辉,朱凤芹.时频域自适应均值脊波变换滤波及检测方法.哈尔滨工程大学学报,2010; 31(9):1172—1178
6朱广平,王飞,孙辉.时频域滤波沉底目标亮点特征提取方法研究.哈尔滨工程大学学报,2015; 36(5):616-622
7李秀坤,杨阳,孟祥夏.水下目标回波与混响的时频形态特征域盲分离.声学学报,2017; 42(2):169—177
8 Li W,Subrahmanya N,Xu F.Online subspace and sparse filtering for target tracking in reverberant environment.IEEE 7th Sensor Array and Multichannel Signal Processing Workshop,Hoboken,NJ,USA,2012:329-332
9 Ge F X,Chen Y,Li W.Target detection and tracking via structured convex optimization.IEEE International Conference on Acoustics,Speech and Signal Processing,2017:426-430
10 Yin J,Liu B,Zhu G et al. Moving target detection using dynamic mode decomposition.Sensors,2018; 18(10):3461
11李轶南,张雄伟,贾冲,陈亮,曾理.稀疏低秩噪声模型下无监督实时单通道语音增强算法.声学学报,2015;40(4):607-614
12 Cohen L.Time-frequency distributions-a review.Proc.IEEE,1989; 77(7):941—981
13 Choi H I,Williams W J.Improved time-frequency representation of multicomponent signals using exponential kernels.IEEE Transactions on Acoustics Speech Signal Processing, 1989;37(6):862—871
14 Jones D L,Bariniuk R G.An adaptive optimal-kernel timefrequency representation.IEEE International Conference on Acoustics,Speech,and Signal Processing,Minneapolis,MN,USA,1993(4):109-112
15姚玉玲,王宁,石洪华等.水声信号时频分析方法比较及应用研究.中国海洋大学学报(自然科学版),2011;41(11):115—119
16王晓凯,高静怀,何洋洋.基于时频自适应最优核的时频分析方法.系统工程与电子技术,2010; 32(1):22-26
17倪怀发,沈肖波,孙权森.基于低秩分解的鲁棒典型相关分析.智能系统学报,2017; 12(4):491-497
18马原,吕群波,刘扬阳等.基于主成分变换的图像稀疏度估计方法.物理学报,2013; 62(20):204202
19蔡念,周杨,刘根等.鲁棒主成分分析的运动目标检测综述.中国图像图形学报,2016; 21(10):1265-1275
20李亚安,王军,李钢虎.基于自适应高斯核函数时频分布的水声信号处理研究.系统仿真学报,2006; 18(11):3230-3233
21 Wright J,Ganesh A,Rao S et al.Robust principal component analysis:Exact recovery of corrupted low-rank matrices via convex optimization.Proceedings of the Advances in Neural Information Processing Systems,Vancouver,BC,Canada,2009:2080—2088
22 Candes E J,Li X,Ma Y et al.Robust principal component analysis? J.ACM,2011; 58(3)
23朱兆彤,彭石宝,许稼,许肖梅.水下目标亮点拓扑特征提取及自动识别方法.声学学报,2018; 43(2):154-162
24范军,朱蓓丽.非刚性表面声呐目标回波的修正几何亮点模型.声学学报,2001;26(6):545-550
25李子高,李淑秋,闻疏琳.基于无人平台的水下目标自动检测方法.哈尔滨工程大学学报,2017; 38(1):103-108
2郭国强,杨益新,孙超,李博.浅海低频本地海底混响的波导不变性结构及抑制方法研究.声学学报,2009; 34(6):506-514
3汤渭霖.声呐目标回波的亮点模型.声学学报,1994; 19(2):92—100
4陈云飞,李桂娟,王振山等.水中目标回波亮点统计特征研究.物理学报,2013;62(8):084302
5朱广平,孙辉,朱凤芹.时频域自适应均值脊波变换滤波及检测方法.哈尔滨工程大学学报,2010; 31(9):1172—1178
6朱广平,王飞,孙辉.时频域滤波沉底目标亮点特征提取方法研究.哈尔滨工程大学学报,2015; 36(5):616-622
7李秀坤,杨阳,孟祥夏.水下目标回波与混响的时频形态特征域盲分离.声学学报,2017; 42(2):169—177
8 Li W,Subrahmanya N,Xu F.Online subspace and sparse filtering for target tracking in reverberant environment.IEEE 7th Sensor Array and Multichannel Signal Processing Workshop,Hoboken,NJ,USA,2012:329-332
9 Ge F X,Chen Y,Li W.Target detection and tracking via structured convex optimization.IEEE International Conference on Acoustics,Speech and Signal Processing,2017:426-430
10 Yin J,Liu B,Zhu G et al. Moving target detection using dynamic mode decomposition.Sensors,2018; 18(10):3461
11李轶南,张雄伟,贾冲,陈亮,曾理.稀疏低秩噪声模型下无监督实时单通道语音增强算法.声学学报,2015;40(4):607-614
12 Cohen L.Time-frequency distributions-a review.Proc.IEEE,1989; 77(7):941—981
13 Choi H I,Williams W J.Improved time-frequency representation of multicomponent signals using exponential kernels.IEEE Transactions on Acoustics Speech Signal Processing, 1989;37(6):862—871
14 Jones D L,Bariniuk R G.An adaptive optimal-kernel timefrequency representation.IEEE International Conference on Acoustics,Speech,and Signal Processing,Minneapolis,MN,USA,1993(4):109-112
15姚玉玲,王宁,石洪华等.水声信号时频分析方法比较及应用研究.中国海洋大学学报(自然科学版),2011;41(11):115—119
16王晓凯,高静怀,何洋洋.基于时频自适应最优核的时频分析方法.系统工程与电子技术,2010; 32(1):22-26
17倪怀发,沈肖波,孙权森.基于低秩分解的鲁棒典型相关分析.智能系统学报,2017; 12(4):491-497
18马原,吕群波,刘扬阳等.基于主成分变换的图像稀疏度估计方法.物理学报,2013; 62(20):204202
19蔡念,周杨,刘根等.鲁棒主成分分析的运动目标检测综述.中国图像图形学报,2016; 21(10):1265-1275
20李亚安,王军,李钢虎.基于自适应高斯核函数时频分布的水声信号处理研究.系统仿真学报,2006; 18(11):3230-3233
21 Wright J,Ganesh A,Rao S et al.Robust principal component analysis:Exact recovery of corrupted low-rank matrices via convex optimization.Proceedings of the Advances in Neural Information Processing Systems,Vancouver,BC,Canada,2009:2080—2088
22 Candes E J,Li X,Ma Y et al.Robust principal component analysis? J.ACM,2011; 58(3)
23朱兆彤,彭石宝,许稼,许肖梅.水下目标亮点拓扑特征提取及自动识别方法.声学学报,2018; 43(2):154-162
24范军,朱蓓丽.非刚性表面声呐目标回波的修正几何亮点模型.声学学报,2001;26(6):545-550
25李子高,李淑秋,闻疏琳.基于无人平台的水下目标自动检测方法.哈尔滨工程大学学报,2017; 38(1):103-108