A beam interpolation algorithm in digital multi-beam sonar

I.IntroductionThedigita1operationins0narsystembringsagreatadvantageinbeamforming.Itispos-sib1etorealizesomcalg0rithmsindigita1technique,whichisusua11yhardtoorimpossib1etoAnp1ementintraditiona1ana1ogues0nar.Asanexamp1e,ageometrygraphofcirclearrayisil-1ustratedinFig,1.Thetotalnumber0farraye1ementisN.Therearetwokindsofbeam.TheA-c1assbeamconsistsof2M-le1ements,thesteeringdirectionisjustlocatedintheposi-honofoneofthearrayelement;TheB-classbeamconsistsof2Me1ementsandthesteeringdirectionisinthem…     

微弱信号源的和波束定向方法与分裂波束定向方法的性能比较

本文讨论水下辐射噪声源的精确定向问题，给出被动声纳和波束定向与分裂波束定向方法的性能比较。指出在一定信噪比下，分裂波束精确测向技术比和波束定向技术具有较高的定向精度。但是，随着信噪比的下降，两者趋于一致。推导了估计定向精度的分析表达式，给出在直线阵和圆弧阵情况下，延时估计和声源入射角偏差之间的换算公式和数值模拟结果。同时给出数字式声纳用以计算入射信号左波束和右波束数据的互谱来实现分裂波束定向的方法。     

矢量声纳高速运动目标稳健高分辨方位估计

针对水声矢量信号处理框架中的高速运动目标低信噪 比小快拍条件下的稳健高分辨方位估计问题, 将压缩感知技术应用于水声矢量信号空间谱估计模型中. 结合声矢量传感器结构特性, 探讨了基于声压振速联合处理的广义时域滤波方法; 结合矩阵空域预滤波理论, 设计了基于阻带约束通带均方误差最大值最小的空域滤波器, 研究了矢量声纳空域预滤波方法; 结合以上分析, 提出了基于压缩感知技术的时空联合滤波高分辨方位估计方法, 给出了方法的数学模型、物理解释及具体实施步骤.理论分析和计算机仿真试验表明, 新方法对于小快拍数 条件下的矢量声纳高速运动目标高分辨方位估计问题, 具有较低的双目标分辨门限和较高的估计精度, 有着良好的应用前景.湖上试验验证了方法的有效性. 关键词： 声矢量传感器 空间谱估计 时空滤波 压缩感知     

数字多波束声呐的一种内插算法

数字多波束声响的每一波束具有固定指向．为了在空间域得到较高的分辨力，减少声呐设计中的硬件开销，波束成形后的内插是十分必要的．本文基于抛物线三点与五点内插的原理，提出一种简单易行，精度较高的波束内插算法，并以直线阵和圆弧阵为例，说明这种算法的实现方法。实际计算机模拟结果表明，本文提出的算法可以改善多波束系统的指向特性，提高对目标的分辨精度．     

交叉验证多路径匹配追踪水声矢量阵稀疏方位估计

水下运动目标的高分辨DOA估计和目标的左右舷分辨问题一直是水声阵列信号处理中的一个核心问题。矢量阵相比于声压阵具有天然的左右舷分辨能力和更高的处理增益,近年来得到了广泛关注。Capon等一些传统高分辨处理方法存在不能解相干源、需要多快拍处理以及对阵列流行误差敏感等多种问题。针对水声阵列信号处理领域面临的以上问题,利用声呐工作场景中空间目标的稀疏性,本文提出了一种基于交叉验证技术的多路径匹配追踪（Multiplepath Matching Pursuit with Cross Validation,CV-MMP）声矢量阵稀疏DOA估计算法。该算法采用交叉验证技术可以在未知场景中目标个数的条件下实现稀疏DOA的估计,相比于常规的声矢量阵Capon算法而言,可以在小快拍数甚至单快拍数条件下实现多目标的稀疏DOA估计以及高分辨能力。仿真和海试试验数据处理验证了提出的算法的有效性。      

Experimental verification of an interpolation algorithm for improved estimates of animal position

This article presents experimental verification of an interpolation algorithm that was previously proposed in Jaffe [J. Acoust. Soc. Am. 105, 3168-3175 (1999)]. The goal of the algorithm is to improve estimates of both target position and target strength by minimizing a least-squares residual between noise-corrupted target measurement data and the output of a model of the sonar's amplitude response to a target at a set of known locations. Although this positional estimator was shown to be a maximum likelihood estimator, in principle, experimental verification was desired because of interest in understanding its true performance. Here, the accuracy of the algorithm is investigated by analyzing the correspondence between a target's true position and the algorithm's estimate. True target position was measured by precise translation of a small test target (bead) or from the analysis of images of fish from a coregistered optical imaging system. Results with the stationary spherical test bead in a high signal-to-noise environment indicate that a large increase in resolution is possible, while results with commercial aquarium fish indicate a smaller increase is obtainable. However, in both experiments the algorithm provides improved estimates of target position over those obtained by simply accepting the angular positions of the sonar beam with maximum output as target position. In addition, increased accuracy in target strength estimation is possible by considering the effects of the sonar beam patterns relative to the interpolated position. A benefit of the algorithm is that it can be applied "ex post facto" to existing data sets from commercial multibeam sonar systems when only the beam intensities have been stored after suitable calibration.     

MIMO声呐的双尺度旋转不变子空间波达方向估计*

为了提高单基地多输入多输出(Multiple-Input Multiple-Output, MIMO)声呐阵列的波达方向(Direction of arrival, DOA)估计性能,提出了双尺度旋转不变子空间(Dual-Resolution Estimation of Signal Parameters via Rotational Invariance Techniques, DR-ESPRIT)算法。结合MIMO阵列虚拟阵列的结构特征,首先利用ESPRIT算法通过各条虚拟线阵内、基线间距不大于半波长的子阵间的旋转不变关系得到无模糊的粗估计结果,之后利用虚拟线阵间、基线较长的子阵间的旋转不变关系得到一组有模糊的精估计结果。参考粗估计结果对精估计结果进行解模糊,最终得到高精度无模糊的角度估计结果。为了降低运算复杂度,利用该思路对降维ESPRIT算法也进行改进,提出了双尺度降维ESPRIT算法。仿真试验首先验证了与传统算法相比,双尺度类DOA估计算法能够有效提高角度估计精度。此外,还分析了MIMO声呐阵列的发射、接收阵元的幅相扰动误差对算法角度估计性能的影响。     

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。      

Sparse asymptotic minimum variance based bearing estimation algorithm for a single vector hydrophone

For underwater target detection using a single vector hydrophone, sparse asymptotic minimum variance(SAMV) method is used to estimate the target bearing. The SAMV discretizes the entire scanning space and the target bearing is located at the position of the discrete direction. The SAMV algorithm utilizes the sparsity of the spatial signal to improve the estimation performance of the target bearing. Background noise level(BNL) of the bearing estimation of SAMV algorithm is lower than those of the conventional beam forming(CBF)method and minimum variance distortionless response(MVDR) method for different signal noise ratios(SNRs). When the SNR is higher than 0 d B, the direction-finding error of this algorithm is less than 2°. Moreover, the SAMV algorithm has a better dimensional orientation resolution capability. The experimental results show that the SAMV algorithm gives a bearing and time recording map with a lower BNL, which effectively verifies the effectiveness of SAMV algorithm in terms of underwater target detection.     

On fast estimation of direction of arrival for underwater acoustic target based on sparse Bayesian learning

The Direction of Arrival(DOA) estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning(TMSBL) as the reconstructing algorithm have the disadvantage of slow computing speed.To solve this problem,a fast underwater acoustic target direction of arrival estimation was proposed.Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation,an algorithm was proposed to obtain the value of core hyper-parameter through MacKay's fixed-point method to estimate the DOA.By this process,it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm.Simulation results verified the feasibiUty and effectiveness of the proposed algorithm.     

宽带双曲调频信号参数化时频分析波达方向估计

针对有源探测或脉冲侦查中双曲调频信号的波达方向估计问题,提出了基于参数化时频变换(PTFT)的多重信号分类(MUSIC)测向算法,简称PTFT-MUSIC算法。该算法由发射信号确定针对双曲调频信号的参数化变换核,对接收信号进行频域参数化时频变换,利用获得的时频分布建立阵列信号时频分布模型,并以此模型设计基于时频分布矩阵的MUSIC算法以实现双曲调频信号的波达方向估计。通过仿真和实验对该算法的估计误差和多目标分辨性能进行了分析,仿真和海上实验结果表明:相比现有的时频MUSIC算法,PTFT-MUSIC算法能有效提高空间谱分辨率和波达方向估计性能,同时该算法拥有对特定调频信号筛选性,结合时频域滤波算法能有效抑制相干直达波干扰,应用于多基地声呐系统时有效提高了声呐定位性能。     

点云投影结合轻量化卷积神经网络实现三维成像声呐快速目标分类*

三维成像声呐的成像结果是三维点云,基于点云的三维成像声呐目标分类方法具有网络结构复杂,计算量大的特点,针对这一问题本文提出了一种将三维成像声呐成像结果从三维点云投影至二维图像的方法,并且使用轻量化卷积神经网络实现了三维成像声呐快速目标分类。该方法首先对三维成像声呐波束形成后的波束域数据进行最大值滤波和阈值滤波,降低点云数据维度;接着,依据三维成像声呐的波束方向,将点云投影为深度图和强度图,分别保存点云的位置信息和强度信息;然后,利用深度图和强度图分别作为第一个通道和第二个通道构建混合通道图,将混合通道图作为目标分类网络的输入,从而将三维点云的目标分类问题转换为二维图像的目标分类问题;最后使用MobileNetV2网络实现了三维成像声呐快速目标分类。实验结果表明,通过本文提出的投影方法可以用二维图像分类网络完成三维成像声呐点云的目标分类任务;而且混合通道图比单独的强度图和深度图收敛速度更快,结合目标识别网络可以25fps实时的进行目标分类,在真实数据集上分类精度达到了91.13%。     

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.     

进行子阵加权波束形成的波达方向估计

针对水声信道的多径效应以及海底散射信号信噪比低导致方位估计性能较差的问题,提出了一种基于子阵加权波束形成的UESPRIT算法(Weighted Beamspace UESPRIT Based on Subarrays,BS-BUESPRIT)。首先利用密集波束域转换矩阵估计回波信号的方位谱,进而估计同一时刻到达阵列的回波数目;之后将均匀线阵分为多个尺寸相同、相互重叠的子阵,利用加权波束形成对各子阵接收信号做指定方向的空域滤波;最后基于各子阵波束形成后的输出结果,利用UESPRIT算法实现回波方向的估计。仿真和湖试、海试试验结果表明,与UESPRIT算法相比,BS-BUESPRIT算法提高了信号波达方向估计性能,在多径和较低信噪比条件下有着更高的估计精度,应用于高分辨率测深侧扫声呐时有效地提高了声呐的测深性能。      

一种基于多普勒频移的被动合成孔径声纳探测方法*

由于自主小平台声纳孔径有限,对声纳探测的分辨率的提高有所限制。小平台的机动可以有效的与声纳探测方法相结合来提高声纳探测性能。针对这一特点,提出一种基于多普勒频移技术的被动合成孔径声纳探测方法。该方法根据自主小平台的机动所引起声纳的接收信号多普勒频移的变化,进行目标的频率与方位联合估计。本文将自主小平台的机动引入到波束形成技术当中形成一种新的被动合成孔径技术。数值仿真表明,该方法可以有效的进行目标方位估计,并且获得较高的方位分辨率,改善了自主小平台的探测性能。     

Multiple ping sonar accuracy improvement using robust motion estimation and ping fusion

Noise degrades the accuracy of sonar systems. We demonstrate a practical method for increasing the effective signal-to-noise ratio (SNR) by fusing time delay information from a burst of multiple sonar pings. This approach can be useful when there is no relative motion between the sonar and the target during the burst of sonar pinging. Otherwise, the relative motion degrades the fusion and therefore, has to be addressed before fusion can be used. In this paper, we present a robust motion estimation algorithm which uses information from multiple receivers to estimate the relative motion between pings in the burst. We then compensate for motion, and show that the fusion of information from the burst of motion compensated pings improves both the resilience to noise and sonar accuracy, consequently increasing the operating range of the sonar system.     

改进可变区域拟合模型的合成孔径声呐图像分割方法

为了自动且高精度地分割合成孔径声呐图像中的目标和阴影区域,提出一种核函数尺度自适应可变区域拟合(RSF)模型的分割方法。使用一种基于K-均值聚类的自动初始化方法对水平集进行初始化,减少了人为干预;提出改进的核函数尺度自适应RSF模型,其利用声呐成像中目标与阴影在沿扫测方向具有近似宽度的一般规律,自适应选择核函数尺度参数,使得对应目标和阴影的水平集函数能够同步演化,提高最终分割精度。通过对真实声呐图像的实验结果分析,验证了该方法能较为准确地实现目标和阴影区域的分割,具有一定的精确性和适应性。      

波束形成与独立分量分析融合的宽带高分辨方位估计方法

针对波束域高分辨方位估计存在方位估计偏差大等缺点,提出了将波束形成与独立分量分析融合的宽带高分辨方位估计方法:(1)阵元域数据转换到波束域后,对多波束数据利用基于二阶统计量的独立分量分析算法处理,(2)将波束域独立分量分析融合方法输出结果重构回阵元域信号,(3)对重构后的阵元域数据,使用约束最优化方法改进的MUSIC算法做高分辨方位估计。最后利用ICS (Interactre Circuts&System)声呐模拟器数据和海试数据对算法进行了验证。结果表明:本文方法在弱目标检测、观测区域外强干扰抑制、方位分辨率方面都优先于波束域MUSIC和波束MVDR方法。      

A tracking filter method of active sonar subject to unknown target maneuver

A tracking filter algorithm based on the maneuvering detection delay is presented in order to solve the fuzzy problem of target maneuver decision introduced by the measure?ment errors of active sonar. When the maneuvering detection is unclear, two target moving hypotheses, the uniform and the maneuver, derived from the method of multiple hypothesis tracking, are generated to delay the final decision time. Then the hypothesis test statistics is constructed by using the residual sequence. The active sonar?s tracking ability of unknown prior information targets is improved due to the modified sequential probability ratio test and the integration of the advantages of strong tracking filter and the Kalman filter. Simulation results show that the algorithm is able to not only track the uniform targets accurately, but also track the maneuvering targets steadily. The effectiveness of the algorithm for real underwater acoustic targets is further verified by the sea trial data processing results.     

虚拟阵列Khatri-Rao积与子空间联合稀疏表示的DOA估计方法

利用目标信号在空域分布的稀疏性,该文提出了一种基于虚拟阵列Khatri-Rao(KR)积与信号子空间联合稀疏表示的单快拍DOA估计方法;该方法利用单次快拍的采样数据,构造出双向虚拟阵列数据,并对虚拟阵列数据的协方差矩阵进行KR积变换处理,然后对向量化后的数据进行顺序重构,利用重构矩阵的大奇异值对应的左奇异向量为估计信号子空间;最后,利用凸优化工具箱对稀疏模型进行二阶凸规划的优化求解,得到高精度的DOA估计值;仿真实验验证了算法的有效性,在低信噪比下比传统MUSIC和OMP算法具有更高的估计精度。