基于声压振速联合处理的声矢量阵信源数检测与方位估计

为解决水下远程测向问题,首先论述了基于声压与振速互协方差矩阵的声矢量阵特征子空间方法,然后利用空时虚拟抽头处理,提出了一种基于特征向量的信源数检测与子空间划分准则。理论分析表明,与现有的将声矢量传感器的振速信息作为独立阵元来处理的声矢量阵测向方法不同,新的信源数检测与方位估计方法完全基于声压与振速联合信息处理,能将子空间方法的高分辨能力与声矢量阵的抗噪能力有机结合起来,可实现对远程目标的高分辨检测与定向。基于湖试数据的仿真实验证明了所述方法的有效性。     

声矢量圆阵相位模态域目标方位估计

为了实现矢量传感器在圆阵阵型下的应用,文中提出了一种适合于声矢量圆阵的目标方位估计算法。该算法首先将声矢量圆阵阵元域信号分解为一系列相互正交的相位模态,在相位模态域构造声压和质点振速的互协方差矩阵,然后进行MUSIC方位估计.理论分析和仿真结果表明,文中算法比相同阵型的声压阵MUSIC方位估计算法具有更好的噪声抑制能力、方位估计性能以及多目标分辨能力,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方位估计性能。该算法实现了声压和质点振速的相干处理,充分利用了声矢量传感器的平均声强抗噪原理,具有较强的抗各向同性噪声能力,并可以将子空间类DOA(Direction of Arrival)估计算法和相位模态域阵列信号处理技术有机结合起来,实现了声矢量传感器在圆阵阵型条件下的高分辨DOA估计。     

柱形障板条件下声矢量圆阵相位模态域目标方位估计

提出了基于横向、纵向和对角排列的声压振速联合处理协方差矩阵构造的相位模态域最小方差无畸变响应(MVDR)目标方位估计方法。该方法利用弹性柱障散射声场模型,根据声矢量圆阵相位模态变换理论,构造声压与不同振速分量的互协方差矩阵,并进行相加、纵向、横向和对角排列得到扩展互协方差矩阵,将扩展互协方差矩阵分解重构后进行MVDR目标方位估计。理论分析与仿真结果表明,纵向排列互协方差矩阵具有更好的方位估计性能,相加互协方差矩阵具有较低的背景谱,对角和横向排列相对纵向排列较差。水池试验和湖试结果进一步验证了纵向排列互协方差矩阵具有较其他3种方法更好的方位估计性能。     

基于超指向性多极子矢量阵的水下低频声源方位估计方法研究

针对水下低频声源的方位估计问题,基于基元紧密排列的二维矢量阵,建立了一种超指向性波束形成方法.根据矢量基元差分运算构建各阶多极子模型,获得了几乎与频率无关的模态函数,并经加权计算可在低频条件下实现超指向性波束,以解决阵列孔径对波束性能的限制.同时,结合输出信噪比最大准则所得波束,分析了超指向性波束形成算法的稳定性与波导的影响程度,探索模态阶数与阵列参数的选取原则.通过阵列性能的仿真计算与实际阵列的测量数据表明,该算法可在小尺寸阵列孔径下获得良好的阵列波束,兼具了水下线型超指向性阵和环形超指向性阵的优点,可有效实现水下低频声源的水平方位估计;且波束性能可通过调节模态阶数与基元间距以达到最佳,并受水下声波导多途与频散效应影响有限.     

基于矢量水听器阵列空域预滤波处理的多重信号分类算法研究

矢量水听器能同时拾取声压和振速信息,在相同的信噪比、阵元数及阵列孔径下,矢量阵定向性能优于声压阵列。目前,以多重信号分类算法(Multiple signal classification,MUSIC)为代表的高分辨定向算法已经广泛应用于矢量水听器阵列中。但是随着信噪比降低、信号源方位间隔减小,传统MUSIC算法定向精度及分辨概率显著下降。本文采用最小二乘法设计适用于矢量水听器水平阵列的矩阵空域滤波器,用于阵列数据的空间滤波预处理,可以对阻带扇面噪声进行有效抑制。由滤波后的数据协方差矩阵可以得到新的噪声子空间,在传统MUSIC算法基础上修正通带扇面内阵列流型的畸变后即可得到滤波后MUSIC算法的方位谱。仿真结果表明,当信噪比较低时,改进算法有效提高了通带扇面内目标方位分辨性能。最后本文对四基元矢量水平阵列海试数据进行了处理,改进算法对窄带信号定向较常规算法-3 dB束宽减小了13°,旁瓣级降低约8 dB。对有一定带宽的行船辐射噪声定向处理得到了更加精确的航迹图,海试数据处理结果证明了该算法的可行性和有效性。     

声矢量锥形阵的高阶累积量波达方向估计

为解决信源在较低信噪比情况下的测向分辨率问题,提出阵列可扩展的声矢量锥形阵测向算法。算法基于四阶累积量的阵列扩展和高斯噪声抑制特性,计算声矢量传声器不同输出分量的四阶累积量,使其在三维方向上扩展与原阵型结构相同的虚拟阵,从而构造包含角度信息的旋转不变矩阵进行测向。推导给出了算法的克拉美罗界,理论分析了算法性能受信噪比、采样快拍以及入射声源俯仰角的影响。仿真实验验证了该算法较常规声矢量阵ESPRIT算法有更优的噪声抑制能力及更高分辨的DOA估计性能。     

Direction-of-arrival estimation for extensible acoustic vector sensor array

Aiming at high-resolution estimation of the direction-of-arrival of closely-spaced sources at low signal-to-noise ratio regions, this paper proposes a DOA estimation algorithm that is suitable for an extensible acoustic vector sensor array. Taking the 3D array composed of the minimum number(four) of acoustic vector sensors as the acquisition module, a virtual array having the same structure as the original array structure is extended in the three-dimensional space based on the aperture expansion...     

Wideband high-resolution direction of arrival estimation method based on the pressure-velocity combined processing using the acoustic vector sensor array

In order to solve the problem of DOA (Direction of Arrival) estimation of underwater distant wideband targets, a novel coherent signal-subspace method based on the cross spectral matrix of pressure and particle velocity using the Acoustic Vector Sensor Array (AVSA) is proposed in this paper. The proposed method is different from existing AVSA based DOA estimation methods in using particle velocity information of Acoustic Vector Sensor (AVS) as an independent array element. It is entirely based on the combined information processing of pressure and particle velocity, namely, the P-V cross spectrum, has better DOA estimation performance than existing methods in isotropic noise field. By theoretical analysis, both focusing principle and eigendecomposition theory based on the P-V cross spectral matrix are given. At the same time, the corresponding criteria for source number detection is also presented. Computer simulations with data from lake trials demonstrate that the proposed method is effective and obviously outperforms existing methods in resolution and accuracy in the case of low Signal-to-Noise Ratio (SNR).     

基于矢量传感器的频率估计算法在水声通信中的应用

本文给出了基于矢量传感器的ESPRIT频率估计算法，并将其应用于频率调制水声通信系统中。与声强频率估计算法相比，ESPRIT频率估计可在小样本的情况下，获得高精度的频率估计。仿真和湖试结果表明，基于矢量传感器的ESPRIT频率估计算法可以提高通信速率并降低对通信系统的带宽要求。本算法对信噪比的要求较高，目前看，较适用于近程高速水声通信。     

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.     

一种声矢量阵子空间拟合算法及其矩阵预滤波器设计

为了减少矩阵预滤波器通带响应畸变程度,提出了一种基于阻带约束通带最大k个误差范数和(k为约束点的数目)最小化准则的矩阵预滤波器设计方法,通过使通带内最大的k个响应误差范数和最小化,减少了通带响应畸变程度。同时针对声矢量阵方位估计算法计算量过大的问题,提出了一种基于矩阵预滤波的声矢量阵子空间拟合算法,通过对声矢量阵声压、振速信号子空间进行联合处理,将预滤波矩阵和拟合子空间矩阵均减少为M×M维(M为阵元数),从而减少矩阵预滤波器设计和方位搜索所需时间。仿真结果和分析表明该算法性能和预滤波MUSIC算法相当,但在计算量上有较大优势,矩阵预滤波方法能抑制阻带干扰,提高后续算法的分辨和估计性能。     

十字阵短时宽带声源实时定向算法

面向短时宽带声源实时定向问题,提出了一种基于互功率谱时延估计的十字阵定向优化算法。针对该方法估计结果离散且呈不均匀分布的特点,将观测平面划分为四个测量区域,并利用不同阵元组合分别处理,解决实时性与估计成功率的矛盾;依据互相关函数的特点,设计了若干判断准则,排除由于数据取样短造成的异常时延估计,改善算法的可靠性;采用频域插值方法,一定程度上提高时延估计精度,从而提高定向精度。MATLAB仿真和DSP系统实验表明,这种方法在实际应用中有效提高了对短时宽带声源定向的性能。     

Detection of number of sources and DOA estimation based on the combined information processing of pressure and particle velocity using acoustic vector sensor array

In order to solve the problem of DOA(direction of arrival)estimation of underwater remote targets,a novel subspace-decomposition method based on the cross covariance matrix of the pressure and the particle velocity of acoustic vector sensor arrays(AVSA)was proposed. Whereafter,using spatio-temporal virtual tapped-delay-line,a new eigenvector-based criteria of detection of number of sources and of subspace partition is also presented.The theoretical analysis shows that the new source detection and direction finding method is different from existing AVSA based DOA estimation methods using particle velocity information of acoustic vector sensor(AVS)as an independent array element.It is entirely based on the combined information processing of pressure and particle velocity,has better estimation performance than existing methods in isotropic noise field.Computer simulations with data from lake trials demonstrate,the proposed method is effective and obviously outperforms existing methods in resolution and accuracy in the case of low signal-to-noise ratio(SNR).     

基于误差修正的传感器阵列水声定位算法

本文分析了海洋环境中定位精度下降的根源,提出了一种基于误差修正的传感器阵列自主定位算法,并进行了计算机仿真和数据分析,结果表明该算法对于提高传感器阵列自主定位精度是有效的。