非均匀对角减载最小方差无失真响应多目标分辨
|
摘要
针对最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)方法在起伏非相关噪声环境下多目标分辨性能严重下降的问题,提出一种非均匀对角减载MVDR(Inhomogeneous Diagonal Unloading MVDR,IDU-MVDR)方法。该方法首先对协方差矩阵进行非均匀对角减载,然后实施MVDR方法。各阵元上的对角减载量通过求解半正定优化问题获得,优化问题中最大化减载量之和,但约束减载后协方差矩阵的最小特征值是一个较小的正值。数值仿真表明,IDUMVDR方法可通过非均匀对角减载消除大部分非相关噪声,但保留小部分噪声分量.因此IDU-MVDR方法较MVDR方法分辨力更高,空间谱中背景级更低、弱目标谱峰更加明显,并且具备一定的稳健性.海上实验结果与数值仿真相一致,验证了IDU-MVDR方法的有效性.
Under some underwater situations,the multi-source resolving performance of the Minimum Variance Distortionless Response(MVDR)method could seriously degrade due to the strong and inhomogeneous uncorrelated noise on the hydrophone array.To mitigate this problem,we propose an Inhomogeneous Diagonal Unloading MVDR(IDUMVDR)method,where inhomogeneous diagonal unloading is applied on the covariance matrix before applying the MVDR method.The diagonal unloading level for each array element is obtained by solving a positive semi-definite optimization problem,where we maximize the sum of the diagonal unloading levels while constraining the minimum eigenvalue of the unloaded covariance matrix to be a small positive value.The numerical simulations suggested that the proposed IDU-MVDR method can remove most of the uncorrelated noise while remaining a small amount of which on the array elements.In consequence,the IDU-MVDR method provides higher resolution,lower background level,more distinct peaks and guaranteed robustness compared to the MVDR method.The sea trial results agreed well with the numerical simulations and proved the vitality of the IDU-MVDR method.
Under some underwater situations,the multi-source resolving performance of the Minimum Variance Distortionless Response(MVDR)method could seriously degrade due to the strong and inhomogeneous uncorrelated noise on the hydrophone array.To mitigate this problem,we propose an Inhomogeneous Diagonal Unloading MVDR(IDUMVDR)method,where inhomogeneous diagonal unloading is applied on the covariance matrix before applying the MVDR method.The diagonal unloading level for each array element is obtained by solving a positive semi-definite optimization problem,where we maximize the sum of the diagonal unloading levels while constraining the minimum eigenvalue of the unloaded covariance matrix to be a small positive value.The numerical simulations suggested that the proposed IDU-MVDR method can remove most of the uncorrelated noise while remaining a small amount of which on the array elements.In consequence,the IDU-MVDR method provides higher resolution,lower background level,more distinct peaks and guaranteed robustness compared to the MVDR method.The sea trial results agreed well with the numerical simulations and proved the vitality of the IDU-MVDR method.
引文
1 Capon J.High-resolution frequency-wavenumber spectrum analysis.Proc.IEEE,1969; 57(8):1408—1418
2杨益新,孙超,马远良.宽带低旁瓣时域波束形成.声学学报,2003; 28(4):331-338
3蒋飚,朱埜,孙长瑜.阵列指向性二次型约束稳健波束形成算法.声学学报,2006; 31(1):91—95
4游鸿,黄建国,史文涛.基于宽带分裂阵列的水下远程被动多目标检测与定向.声学学报,2009; 34(6):527-532
5闫超,郭良浩,汪福全,葛凤翔.一种鲁棒性的最小方差无失真响应波束形成算法及其应用.声学学报,2011;36(6):605-610
6林鹏,宫在晓,郭永刚,张仁和,李风华.拖线阵机动时的自适应波束形成.声学学报,2013; 38(3):251-257
7 Lasky M,Doolittle R D,Simmons B D et al.Recent progress in towed hydrophone array research.IEEE J.Oceanic Eng.,2004; 29(2):374—387
8马根卯,时胜国,于树华.矢量拖曳线列阵流噪声时一空相关特性研究.哈尔滨工程大学学报,2015; 36(01):57-61
9周彬,赵安邦,龚强等.基于对角减载的水声阵列SMI-MVDR空间谱估计技术.系统工程与电子技术,2014; 36(12):2381-2387
10赵安邦,周彬,宋雪晶等.水声阵列信号处理对角减载技术.哈尔滨工程大学学报,2014; 35(11):1327—1331
11夏麾军,马远良,汪勇,刘亚雄.高增益对角减载波束形成方法研究.声学学报,2016; 41(4):449-455
12夏麾军,马远良,刘亚雄.复杂噪声场下对角减载技术的原理及应用.物理学报,2017; 66(1):152-161
13 Wright J,Ganesh A,Rao S et al.Robust principal component analysis:Exact recovery of corrupted low-rank matri-ces via convex optimization.Advances in Neural Information Processing Systems.2009:2080—2088
14 Candes E J,Li X,Ma Y et al. Robust principal component analysis.J.Acm,2011; 58(3):1—37
15 Amailland S,Thomas J H,Pezerat C et al.Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.J.Acoust.Soc.Am.,2018; 143(4):2152-2163
16 Hald J.Removal of incoherent noise from an averaged cross-spectral matrix.J.Acoust.Soc.Am.,2017; 142(2):846
17 Leclere Q,Totaro N,Pezerat C et al.Extraction of the acoustic part of a turbulent boundary layer from wall pressure and vibration measurements.INTER-NOISE and NOISE-CON Congress and Conference Proceedings,2015;251(1):816—824
18 Dougherty R P.Cross spectral matrix diagonal optimization.Proc.6th Berlin Beamforming Conference,2016
19鄢社锋,马远良著.传感器阵列波束优化设计及应用.北京:科学出版社,2009
20 Van Trees H L.Optimum array processing:Part IV of detection,estimation,and modulation theory.John Wiley&Sons,2004
21 Tian Z,Bell K L,Van Trees H L.A recursive least squares implementation for LCMP beamforming under quadratic constraint.IEEE Trans.Signal Process.,2001; 49(6):1138—1145
22 Grant M,Boyd S,Ye Y.CVX:Matlab software for disciplined convex programming.2008
23 Bendat J S,Piersol A G.Engineering applications of correlation and spectral analysis.New York,Wiley-Interscience,1980
2杨益新,孙超,马远良.宽带低旁瓣时域波束形成.声学学报,2003; 28(4):331-338
3蒋飚,朱埜,孙长瑜.阵列指向性二次型约束稳健波束形成算法.声学学报,2006; 31(1):91—95
4游鸿,黄建国,史文涛.基于宽带分裂阵列的水下远程被动多目标检测与定向.声学学报,2009; 34(6):527-532
5闫超,郭良浩,汪福全,葛凤翔.一种鲁棒性的最小方差无失真响应波束形成算法及其应用.声学学报,2011;36(6):605-610
6林鹏,宫在晓,郭永刚,张仁和,李风华.拖线阵机动时的自适应波束形成.声学学报,2013; 38(3):251-257
7 Lasky M,Doolittle R D,Simmons B D et al.Recent progress in towed hydrophone array research.IEEE J.Oceanic Eng.,2004; 29(2):374—387
8马根卯,时胜国,于树华.矢量拖曳线列阵流噪声时一空相关特性研究.哈尔滨工程大学学报,2015; 36(01):57-61
9周彬,赵安邦,龚强等.基于对角减载的水声阵列SMI-MVDR空间谱估计技术.系统工程与电子技术,2014; 36(12):2381-2387
10赵安邦,周彬,宋雪晶等.水声阵列信号处理对角减载技术.哈尔滨工程大学学报,2014; 35(11):1327—1331
11夏麾军,马远良,汪勇,刘亚雄.高增益对角减载波束形成方法研究.声学学报,2016; 41(4):449-455
12夏麾军,马远良,刘亚雄.复杂噪声场下对角减载技术的原理及应用.物理学报,2017; 66(1):152-161
13 Wright J,Ganesh A,Rao S et al.Robust principal component analysis:Exact recovery of corrupted low-rank matri-ces via convex optimization.Advances in Neural Information Processing Systems.2009:2080—2088
14 Candes E J,Li X,Ma Y et al. Robust principal component analysis.J.Acm,2011; 58(3):1—37
15 Amailland S,Thomas J H,Pezerat C et al.Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.J.Acoust.Soc.Am.,2018; 143(4):2152-2163
16 Hald J.Removal of incoherent noise from an averaged cross-spectral matrix.J.Acoust.Soc.Am.,2017; 142(2):846
17 Leclere Q,Totaro N,Pezerat C et al.Extraction of the acoustic part of a turbulent boundary layer from wall pressure and vibration measurements.INTER-NOISE and NOISE-CON Congress and Conference Proceedings,2015;251(1):816—824
18 Dougherty R P.Cross spectral matrix diagonal optimization.Proc.6th Berlin Beamforming Conference,2016
19鄢社锋,马远良著.传感器阵列波束优化设计及应用.北京:科学出版社,2009
20 Van Trees H L.Optimum array processing:Part IV of detection,estimation,and modulation theory.John Wiley&Sons,2004
21 Tian Z,Bell K L,Van Trees H L.A recursive least squares implementation for LCMP beamforming under quadratic constraint.IEEE Trans.Signal Process.,2001; 49(6):1138—1145
22 Grant M,Boyd S,Ye Y.CVX:Matlab software for disciplined convex programming.2008
23 Bendat J S,Piersol A G.Engineering applications of correlation and spectral analysis.New York,Wiley-Interscience,1980