Analysis of SNR for Acoustic Vector Sensor Linear Array in Volume and Surface-generated Noise Fields

Acoustic vector sensor consists of pressure and particle velocity sensors,which measure the three-dimensional acoustic particle velocity,as well as the pressure at one location at the same time.By preserving the amplitude and phase information of the pressure and particle velocity,they possess a number of advantages over traditional scalar sensors.Signal-to-noise ratio (SNR) gain (which is often called array gain) is one of such advantages and is always interested by all of us.But it is not unchangeable if the spatial correlation of the noise field varies.Much more important,it is difficult to be given if the noise becomes complex.In this paper,spatial correlation of the vector field of isotropic volume-noise and surface-generated noise has been introduced briefly.Based on the results,the combined SNR output of a vector linear array is investigated and the maximum gain is given in the specified noise.Computer simulation shows that the output of one array in the same noise is not the same in different gestures.And then we find the best gesture through SNR calculation and obtain the biggest gain,which has important meaning to guide how to deploy an array in practice.We also should use the array with respect to the characteristics of the real ambient noise,especially in anisotropic noise field.

Array gain for a conformal acoustic vector sensor array:An experimental study

An acoustic vector sensor can measure the components of particle velocity and the acoustic pressure at the same point simultaneously, which provides a larger array gain against the ambient noise and a higher angular resolution than the omnidirectional pressure sensor. This paper presents an experimental study of array gain for a conformal acoustic vector sensor array in a practical environment. First, the manifold vector is calculated using the real measured data so that the effects of array mismatches can be minimized. Second, an optimal beamformer with a specific spatial response on the basis of the stable directivity of the ambient noise is designed, which can effectively suppress the ambient noise. Experimental results show that this beamformer for the conformal acoustic vector sensor array provides good signal-to- noise ratio enhancement and is more advantageous than the delay-and-sum and minimum variance distortionless response beamformers.

Direction-of-arrival estimation for a uniform circular acoustic vector-sensor array mounted around a cylindrical baffle

This work investigates the direction-of-arrival(DOA) estimation for a uniform circular acoustic Vector-Sensor Array(UCAVSA) mounted around a cylindrical baffle.The total pressure field and the total particle velocity field near the surface of the cylindrical baffle are analyzed theoretically by applying the method of spatial Fourier transform.Then the so-called modal vector-sensor array signal processing algorithm,which is based on the decomposed wavefield representations,for the UCAVSA mounted around the cylindrical baffle is proposed.Simulation and experimental results show that the UCAVSA mounted around the cylindrical baffle has distinct advantages over the same manifold of traditional uniform circular pressure-sensor array(UCPSA).It is pointed out that the acoustic Vector-Sensor(AVS) could be used under the condition of the cylindrical baffle and that the UCAVSA mounted around the cylindrical baffle could also combine the anti-noise performance of the AVS with spatial resolution performance of array system by means of modal vector-sensor array signal processing algorithms.

A subspace fitting algorithm of acoustic vector sensor array and corresponding matrix pre-filter design

In order to ease the pass-band response distortion of the matrix pre-filter,a simple approach for designing matrix spatial filter is proposed,which minimizes the sum of the k maximal distortion norm（k is the number of the constraint points）within the pass-band,while constraining the filter response within the stop-band.Considering the costly amount of calculation of the high-resolution methods,an algorithm with small amount of calculation based on matrix pre-filtering and subspace fitting using acoustic vector array（MF-VSSF）is proposed.Through joint processing of signal subspace of both pressure and particle velocity,the pre-filtering matrix and the signal subspace is decreased to M-dimensional（M is the number of array-element）,hence reduces the time-consumption of the matrix pre-filter design and DOA searching.Simulation results show that,the method offers the same performance as MUSIC with pre-filtering,but has much lesser amount of calculation.Moreover,the designed prefilter can efficiently suppress the interference in the stop-band and improve the estimation and resolution performance of successive DOA estimators.

模型误差条件下声矢量圆阵多重信号分类测向改进算法

针对非正规协方差矩阵引起声矢量圆阵多重信号分类(MUSIC)测向算法性能恶化的问题,提出了一种基于奇异值分解的声压振速联合处理MUSIC改进算法。理论分析了阵列响应误差和噪声模型误差对协方差矩阵的正规性及估计性能的影响。模型误差条件下声矢量阵声压振速联合处理的协方差矩阵不再是正规矩阵,改进的MUSIC方法通过对声压振速联合处理的协方差矩阵进行奇异值分解,利用非正规矩阵的左、右奇异向量自身正交的特性,采用奇异向量张成噪声子空间。数值仿真结果表明,改进的MUSIC方法改善了波达方向估计精度和多目标分辨能力,且具有更低、更平坦的空间背景谱。湖上试验进一步验证了改进的MUSIC方法的有效性。

主特征向量加权的声矢量圆阵波束域多重信号分类测向方法

为降低声矢量圆阵方位估计方法的计算复杂度,避免声压振速联合处理需选取观测方向的问题,提出了一种主特征向量加权的声矢量圆阵波束域多重信号分类(MUSIC)测向方法。在信号总功率与噪声功率之比最大的优化准则下,求解得到单矢量传感器最优的加权向量,即主特征向量。结合主特征向量设计相应的波束形成矩阵,并从理论上对声矢量圆阵阵元域和波束域MUSIC的目标方位估计方差进行比较。推导了单矢量传感器的大特征值和信号功率、大特征值对应的特征向量和其导向向量之间的关系。通过仿真实验验证了基于主特征向量波束形成矩阵的有效性,所提方法具有较低的计算复杂度和良好的方位估计性能。水池实验数据分析表明,所提方法能够准确估计目标方位。

均匀圆型声矢量阵的四元数二维波达方向估计

为提高声场空域中目标参数估计的精度,将四元数理论应用于均匀圆型声矢量阵列的二维空间角度估计中,建立了基于四元数模型的信号接收模型,推导了圆型声矢量阵的四元数导向矢量,给出了二维波达角估计的四元数域空间谱算法。考虑算法的软硬件可实现性,理论分析了算法的内存占用空间和计算量。此外,分析了圆阵半径对侧向性能的影响,为实际工作中圆阵的半径选取提供了一定的依据。仿真结果表明,基于四元数模型的MUSIC(Multiple Signal Classification)算法的分辨力较高,抗干扰能力较强,提高了信号参数估计的精度。

基于特征值多阈值修正的声矢量圆阵信源数检测

为了解决水下远程目标被动探测问题,提出了一种声矢量圆阵的信源数检测方法。该方法构造了声矢量圆阵声压与振速联合处理的协方差矩阵,并借鉴图像处理中双阈值检测概念,对协方差矩阵的特征值进行多阈值修正处理,利用信息论检测准则实现了声矢量圆阵的信源数检测。理论分析和仿真实验结果表明,本文算法可以有效地提高信息论检测准则的检测性能。水池试验结果也表明本文算法具有更好的噪声抑制能力以及更好的目标检测性能,进一步验证了算法的有效性。

声矢量圆阵宽带相干信号的方位估计

针对水下目标的远程被动探测问题,提出了一种声矢量圆阵的宽带相干目标方位估计方法.首先,基于子带分解原理将宽带划分为若干不重叠窄带,根据圆阵模式空间变换理论,将声矢量圆阵转换成与频率无关的虚拟直线阵,并采用声压P与振速(V_r+V_φ)联合处理方法构建了每个窄带的互协方差矩阵,通过求和平均实现了宽带接收信号的互协方差矩阵估计;其次,引入一种修正的矢量奇异值分解算法,对接收相干信号的互协方差矩阵进行重构处理,用于解决相干声源的空间分辨问题;最后,利用MUSIC算法实现了声矢量圆阵宽带相干目标的方位估计.理论分析及仿真结果表明,修正的矢量奇异值分解算法较修正前具有更强的空间分辨能力;P×(V_r+V_φ)声压振速联合处理方法较同阵型的声压阵及其他声压振速联合处理方法(即(P+V_c)×V_c、P×V_c)具有更好的背景噪声抑制能力;将P×(V_r+V_φ)声压振速联合处理方法与修正的矢量奇异值分解算法有机地结合起来,可提高宽带相干源的方位估计性能.水池实验结果进一步验证了算法的有效性.

中心对称声矢量圆阵的相干双声源方位估计方法

针对水下2个相干目标的远程探测问题,本文提出了一种基于前后向空间平均的声矢量圆阵方位估计方法。该方法根据中心对称声矢量圆阵的结构特性,将声矢量传感器顺序重新排列建立中心对称的阵列形式。利用声压振速联合处理构建声压与振速的互协方差矩阵,引入后向互协方差矩阵,从而通过平均前向和后向矩阵得到前后向平均的互协方差矩阵。采用最小方差无畸变响应波束形成器完成2个相干目标的方位估计。理论分析与仿真结果表明:与声压处理方法、声压振速独立处理方法以及相位模态域声压振速联合处理方法相比,本文所提方法具有较高的角分辨能力、方位估计精度以及较强的噪声抑制能力;相比相位模态域声压振速联合处理方法,该方法可适用的频带范围更宽。试验结果进一步验证了该方法具有较好的相干双目标方位估计能力。

声矢量圆阵宽带相干目标MVDR方位估计

针对宽带相干目标的远程探测问题,该文提出一种基于声压振速联合处理和矢量重构的声矢量圆阵MVDR波束形成方法。该方法利用相位模态变换技术,将声矢量圆阵变换为与信号频率无关的虚拟线阵,并构建虚拟线阵声压与组合振速的互协方差矩阵,利用声压与振速各分量间的空间相关性有效地抑制各向同性环境噪声;并对宽带相干信号的互协方差矩阵进行矢量重构,即将最大特征值对应的特征向量划分为相互重叠的子向量,从而构建前/后向Hermitian矩阵;最后,基于MVDR波束形成器实现宽带相干目标的方位估计。仿真计算和实验数据处理结果表明,该方法具较强的解相干能力和噪声抑制能力以及较高的方位估计性能。

Joint estimation of DOA, frequency, and polarization based on cumulants and UCA

A joint estimation algorithm of direction of arrival （DOA）, frequency, and polarization, based on fourth-order cumulants and uniform circular array （UCA） of trimmed vector sensors is presented for narrowband non-Gaussian signals. The proposed approach, which is suitable for applications in arbitrary Gaussian noise environments, gives a closed-form representation of the estimated parameters, without spectral peak searching. An efficient method is also provided for elimination of cyclic phase ambiguities. Simulations are presented to show the performance of the algorithm.

非均匀噪声下基于稀疏重构的声矢量传感器阵列方位估计方法

为解决非均匀噪声情况下声矢量传感器阵列方位估计性能恶化的问题。提出基于加权最小二乘(weighted least squares,WLS)的稀疏信号重构法和基于加权协方差矩阵拟合(weighted covariance matrix fitting,WCMF)的稀疏信号重构法。定义一个虚拟的声矢量传感器阵列流形矩阵,并重构包含稀疏信号功率和噪声功率的协方差矩阵。为估计稀疏信号功率和每个通道输出的噪声功率,基于WLS法和稀疏信号加权最小化法,构造了关于稀疏信号功率和噪声功率的代价函数。在此基础上,为了进一步提高稀疏信号功率和噪声功率的估计精度,基于WCMF准则对构造的代价函数进行改进。应用泰勒级数展开式将关于稀疏信号功率和噪声功率的非线性代价函数转化为线性函数,并采用循环迭代算法估计稀疏信号功率和噪声功率,待迭代终止时,对稀疏信号功率谱峰搜索,即可实现对目标的方位估计。仿真结果表明,与现有非均匀噪声下的估计方法相比,所提方法提高了非均匀噪声情况下声矢量传感器阵列的方位估计精度。