基于多重信号分类法的一种声矢量阵方位估计算法

为了提高声矢量阵高分辨方位估计的性能,文中提出了一种矢量阵MUSIC方位估计算法。该算法先构造声矢量阵声压和振速组合输出的互协方差矩阵,然后进行MUSIC方位估计。理论分析和计算机仿真表明,文中算法比传统声矢量阵MUSIC方位估计算法有更好的双目标分辨能力和弱目标方位估计能力,湖试结果也表明文中算法有更好的目标方位估计性能。该算法基于矢量传感器声压和振速的相干性原理,充分利用声压振速组合指向性抗干扰能力,可以更好地抑制各向同性干扰,提高阵列的处理增益,从而有更好的方位估计性能。     

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。     

一种深度学习的立体阵波达方向估计方法

针对常规波束形成主瓣宽且目标分辨能力低的问题,提出一种基于深度卷积神经网络的波达方向估计方法。算法使用常规波束形成计算二维空间功率谱,将预处理后的空间功率谱图输入深度卷积神经网络。该文利用神经网络学习解卷积映射关系,输出主瓣宽度更窄的空间功率谱图,从而实现高分辨率二维波达方向估计。该算法对阵列结构没有限制,适用于立体阵。仿真结果表明该文方法在不同目标个数、快拍数及信噪比参数下均能准确估计目标方向。该文方法目标分辨能力优于常规波束形成方法。在低快拍情况下,目标方向估计误差低于自适应波束形成方法。     

Two-dimensional wideband direction of arrival estimation with acoustic vector-sensor array

A closed-form algorithm for two-dimensional wideband Direction-of-Arrival (DOA) estimation with an acoustic vector-sensor array was presented. The proposed algorithm can deal with coherent sources without loss of array aperture, and does not require a search procedure for the DOA parameters. The estimated azimuth and elevation angles can be automatically paired. Simulation results are presented to demonstrate the effectiveness of the proposed method.     

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).     

一种快速水声目标波达方向估计方法

以基追踪为重构算法的水声目标波达方向估计方法在实际的应用中往往运算速度较慢,针对这一问题,在分析水声目标空间稀疏特性的基础上,结合压缩感知理论框架下波达方向估计的特点,提出一种快速水声目标波达方向估计方法。该方法通过逐步减小逼近参数的方式来得到l₀范数最优解,实现水声目标信号的波达方向估计。通过计算机仿真从成功率、运算时间、分辨角度等多个方面与基追踪算法进行比较分析,实验表明:在成功率和分辨角度上所提出算法与基追踪算法性能相当,但运算时间却仅仅是基追踪算法的1/11。     

Weak signal direction of arrival estimation for colocated multiple-input multiple-output sonar array

In order to suppress the influence of symmetrical noise component on multiple-input multiple-output(MIMO)sonar's direction of arrival(DOA)estimation under the condition of low signal-to-noise ratio,we propose a DOA estimation algorithm based on covariance matrix reconstruction method.Firstly,the noise field can be decomposed into symmetrical noise field and asymmetrical noise field.We utilize symmetry property of colored noise matrix and the feature that the imaginary part of covariance matrix has no relation with the symmetry noise to remove the real part of covariance matrix.This operation helps to suppress the influence of colored noise on DOA estimation accuracy.Based on the principle of the imaginary matrix part displacement and the dimension reduction transformation method,the real part of covariance matrix is reconstructed,which helps to suppress the bilateral spectrum interference.Thereafter,Toeplitz method is applied for the covariance matrix decorrelation amendment,and a noise subspace is formed by singular value decomposition(SVD).Finally,we can estimate the DOA of target signals.Both theoretical analysis results and numerical simulation results verify the symmetrical noise suppression performance of this algorithm,and the estimation performance of target azimuth is improved obviously.This method has the characteristics of lower operational complexity,higher degrees of freedom and stronger target resolution.     

波达方向估计中特征空间的信源数估计方法

提出了特征空间法信源数估计方法,它将阵列信号的协方差估计值分别投影到信号的特征子空间和噪声的特征子空间。由于信号子空间与噪声子空间相互正交,易于由表征投影大小的判据值区分信号和噪声的贡献;本方法用的是M×M阶矩阵特征值分解,M为基元数,与波达方向估计用的相同,因此节省大量的计算量;它可以在实数空间中进行运算,进一步减少运算量。进行了数值计算,检验了判据值分布,以及在信源等功率、不等功率和空间相关色噪声等情况下特征空间法的性能。估计方法还用声纳数据进行了检验。所有这些结果均证明本估计方法性能优良。     

Maximum likelihood estimation of direction of arrival using an acoustic vector-sensor

A vector-sensor consisting of a monopole sensor collocated with orthogonally oriented dipole sensors is used for direction of arrival (DOA) estimation in the presence of an isotropic noise-field or internal device noise. A maximum likelihood (ML) DOA estimator is derived and subsequently shown to be a special case of DOA estimation by means of a search for the direction of maximum steered response power (SRP). The problem of SRP maximization with respect to a vector-sensor can be solved with a computationally inexpensive algorithm. The ML estimator achieves asymptotic efficiency and thus outperforms existing estimators with respect to the mean square angular error (MSAE) measure. The beampattern associated with the ML estimator is shown to be identical to that used by the minimum power distortionless response beamformer for the purpose of signal enhancement.     

加权最大似然波达方向估计算法及其应用研究

针对阵列信号处理中空间相干性低、频域有色且通道间具有相同功率谱的噪声模型,分别基于确定性信号模型与随机性信号模型,提出一种加权最大似然(Weighted Maximum Likelihood,WML)波达方向估计算法。数据仿真实验表明,该算法提高了由空间非相干且一致有色噪声引起的低信噪比条件下的波达方向估计精度。户外实验验证了该算法在风噪声条件下的有效性。     

一种快速稀疏贝叶斯学习的水声目标方位估计方法研究

针对以具有时序结构的稀疏贝叶斯学习(Temporally multiple sparse Bayesian learning,TMSBL)为重构算法的水声目标DOA (Direction-of-arrival)估计方法存在运算速度慢的问题,结合块稀疏贝叶斯学习(Block-spare Bayesian learning,BSBL)理论框架下DOA估计模型与特点,采用MacKay提出的定点方法(Fixed-point method)对TMSBL算法中的核心超参量进行求解,提出一种快速的水声目标方位估计稀疏贝叶斯学习的方法,该方法具有运算速度快,重构概率高的特点,并通过实验仿真从运算时间、失败率和均方根误差等方面与TMSBL算法进行比较,验证了该方法的可行性与有效性。     

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。