对称旋转不变相干分布式非圆信号二维波达方向估计

在相干分布式非圆信号二维波达方向估计中,利用信号非圆特性可提升估计精度,但现有的低复杂度算法利用泰勒级数近似建立的旋转不变关系会引入额外误差.针对该问题,考虑中心对称的三维立体线阵,提出了一种基于对称旋转不变关系的二维波达方向估计算法.算法首先利用信号非圆特性建立了扩展阵列模型;然后证明了对于任意的中心对称阵列,相干分布源的确定性角信号分布函数矢量具有对称特性,利用此特性在三维立体线阵的三个子阵中分别建立了扩展广义方向矢量的对称旋转不变关系;基于此,通过无须搜索的多项式求根方式分别得到中心方位角和俯仰角估计;最后利用整个阵列广义方向矢量的对称旋转不变关系构造代价函数实现了参数匹配.理论分析和仿真实验表明,相比于现有的低复杂度算法,所提算法避免了泰勒级数近似引入的额外误差,以较小的复杂度代价获得了性能的较大提升.同时,所提算法能够实现三维空间全方位的角度估计.     

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。      

基于多个小孔径基阵的水下目标宽带波达方位估计方法

本文将多小孔径基阵应用于声纳浮标阵对可疑海区进行快速搜索的研究中,提出了一种基于多个小孔径子阵的宽带目标方位估计方法.该方法采用一种新颖的降秩估计器的信号模型,对多个小孔径子阵的输出信息进行综合处理,然后使用近似意义上的降秩估计器对目标源进行方位估计.所建立的阵列模型中不需要包含有关小孔径子阵之间的任何位置信息,因此,有效地减小了因阵形估计带来的烦杂工作和估计误差对方位估计性能的影响.仿真结果表明,此方法提高了定向精度,给出了稳定的目标方位估计.     

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。     

一种改进的稀疏近似最小方差DOA估计算法研究

针对稀疏信号的超分辨方位估计问题,提出一种可变因子的稀疏近似最小方差算法(α-Sparse Asymptotic Minimum Variance,简记为SAMV-α)。该算法利用一个折衷参数进行最大似然估计值和稀疏性能的折衷处理,在迭代过程中改变稀疏近似最小方差算法(Sparse Asymptotic Minimum Variance,SAMV)的指数因子,得到强稀疏性能和超低旁瓣的方位谱图,实现邻近目标的超分辨方位估计和相干处理性能,且无需预估角度和信源数目等先验信息,并且折衷参数的取值为0到1之间,取值区间明确,避免了稀疏信号处理算法中正则因子选取困难的弊端。计算机仿真表明SAMV-α算法方位估计性能明显优于波束扫描类算法和子空间类算法,与同类型稀疏信号处理类算法相比仍具有较高的方位估计精度,同时对于邻近声源分辨能力,SAMV-α算法较SAMV-1算法性能提高约3dB。海上试验数据处理给出了分辨率更高的方位时间历程(Bering-Time Recording,BTR)图,有效验证了SAMV-α算法的性能。      

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。      

基于矢量化差分相位的单分布源解耦二维波达方向估计

在分布源(包括相干分布源和非相干分布源)的二维波达方向估计中,均匀圆阵由于可实现全方位测角、具有较高的分辨率,得到了广泛的应用,然而现有的估计算法均需要谱峰搜索和特征值分解,复杂度较高.针对此问题,考虑单个相干分布源或非相干分布源入射两种情况,提出了一种基于矢量化差分相位的解耦二维波达方向快速估计算法.该算法首先基于空间频率近似模型,证明了任意单个分布源入射时,均匀圆阵中不同阵元接收信号间的差分相位均不受角度扩展参数的影响;基于此特性,通过获取差分相位即可实现中心波达角的解耦合;接下来,提取采样协方差矩阵的严格上三角元素相位,即对应于各阵元间的差分相位,并进行矢量化处理,最终将波达方向估计问题转化为一个最小二乘问题,从而直接得到闭式解,避免了谱峰搜索和特征值分解运算,大幅度降低了复杂度.理论分析和仿真实验表明,所提算法具有较高的估计精度,并且无需角信号分布的先验信息,同时具备较低的计算复杂度和硬件复杂度,有利于复杂环境下阵列测向等工程实践.     

水声信号稀疏重构的高阶累积量波达方向估计

高阶累积量具有高斯噪声抑制和阵元扩展特性,将高阶累积量引入水声信号的方位估计中,提出了离格稀疏贝叶斯学习重构的高阶累积量测向算法。该方法利用高阶累积量对高斯噪声的自然盲性,计算阵列信号四阶累积量来滤除高斯噪声,使阵元在原来的结构上扩展了一倍;并构造出选择矩阵剔除了四阶累积量中的冗余项,能再一次的扩展阵元,得到的新观测模型具有更好的统计性能;最后利用空域稀疏性,推导出四阶累积量下的离格稀疏表示模型,采用贝叶斯学习解算出源信号的最大后验概率,实现了目标方位估计。数值仿真和海试实验数据表明,该方法在相邻声源方位间隔为4°的情况下分辨概率可达到95%以上,在信噪比大于-5 dB时目标方位估计的均方根误差在1°以内,可显著抑制背景噪声干扰,在多声源密集分布条件下也能准确、稳健的对水声目标方位进行估计。      

基于压缩感知估计的GIS击穿放电定位方法研究*

经典的空间谱估计方法,如多重信号分离(MUSIC)方法,对噪声敏感,难以在低信噪比环境中有效地进行波达方向估计。为提升在复杂电力环境中的气体绝缘金属封闭开关设备(GIS)击穿定位能力,该文提出了一种基于极化内插的压缩感知波达方向估计方法 CSP-DOA。该方法对传声器阵列接收到的数据进行建模,形成多测量矢量模型,结合压缩感知中稀疏重构技术进行波达方向估计;同时,该方法还采用极化内插技术解决了稀疏重构中的费网格问题,进一步提升了波达方向估计精度及计算效率。通过数值模拟对算法的定位效果进行了分析,仿真结果表明CSP-DOA方法对于击穿信号有更好的定位效果。结合可见光图像匹配实现了GIS击穿信源的二维可视化定位,在某高压大厅的GIS模型上进行耐压击穿定位试验研究,试验结果进一步验证了该文方法可较好的应用于GIS的击穿定位。     

Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (MALLRT)

Various sparse transform models have been explored for compressed sensing-based dynamic cardiac MRI reconstruction from vastly under-sampled k-space data. Recently emerged low rank tensor model using Tucker decomposition could be viewed as a special form of sparse model, where the core tensor, which is obtained using high-order singular value decomposition, is sparse in the sense that only a few elements have dominantly large magnitude. However, local details tend to be over-smoothed when the entire image is conventionally modeled as a global tensor. Moreover, low rankness is sensitive to motion as spatiotemporal correlation is corrupted by spatial misalignment between temporal frames. To overcome these limitations, this paper presents a novel motion aligned locally low rank tensor (MALLRT) model for dynamic MRI reconstruction. In MALLRT, low rank constraint is enforced on image patch-based local tensors, which correspond to overlapping blocks extracted from the reconstructed high-dimensional image after group-wise inter-frame motion registration. For solving the proposed model, this paper presents an efficient optimization algorithm by using variable splitting and alternating direction method of multipliers (ADMM). MALLRT demonstrated promising performance as validated on one cardiac perfusion MRI dataset and two cardiac cine MRI datasets using retrospective under-sampling with various acceleration factors, as well as one prospectively under-sampled cardiac perfusion MRI dataset. Compared to four state-of-the-art methods, MALLRT achieved substantially better image reconstruction quality in terms of both signal to error ratio (SER) and structural similarity index (SSIM) metrics, and visual perception in preserving spatial details and capturing temporal variations.     

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival(DOA) estimation problem for the co-located multiple-input multipleoutput(MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing(CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio(SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification(MUSIC) algorithm and other CS recovery algorithms.     

基于哈达玛积扩展子空间的到达时间和波达方向联合估计

在窄带阵列天线正交频分复用系统的到达时间和波达方向联合估计中, 针对阵元数目较少时波达方向估计精度不高, 特别是多径数目大于阵元数目导致的波达方向无法估计问题, 提出一种基于哈达玛积扩展子空间的到达时间和波达方向联合估计算法. 该算法首先利用各阵元上的频域信道估计构成扩展信道频域响应矢量, 然后计算扩展信道频域响应矢量自相关矩阵, 并进行特征值分解得到哈达玛积扩展噪声子空间, 最后构造伪谱函数并进行二维谱峰搜索, 从而实现到达时间和波达方向的联合估计. 仿真结果表明, 与现有算法相比, 在复杂度没有大幅提高的前提下, 该算法的估计结果均方根误差更加接近克拉美罗界, 且到达时间和波达方向估计能够自动配对, 在多径数目大于阵元数目时依然适用.     

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。     

圆柱阵子阵分级处理的稳健超指向性波束形成方法

提出了圆柱阵子阵分级处理的稳健超指向性波束形成方法。首先建立了圆柱阵分两级子阵进行波束形成的信号模型,接着利用空间均匀噪声场中噪声互谱矩阵的循环特性得到基于特征波束分解与综合模型的圆柱阵超指向性的最优解,然后仿真研究了其误差敏感度函数、阵增益和波束图等性能指标,并与圆柱阵的传统全局处理方法进行了对比。提出的分两级子阵处理的超指向性方法与传统全局处理方法相比不仅降低了数据存储量和波束形成计算量,而且进一步提升了稳健性,并且在低频段的阵增益远远高于常规波束形成的值,对水下声呐阵列的设计具有一定的参考价值。      

水下无源声呐目标听觉域张量特征提取方法

特征提取是水下无源声呐目标分类识别的关键步骤,提出了一种基于听觉Patterson-Holdsworth耳蜗模型的听觉域张量特征提取方法。将耳蜗模型的滤波器冲激响应视为信号分解的基函数,根据听觉模型非线性尺度或常规线性尺度确定不同通道的中心频率,然后计算出相应通道的增益和带宽,并量化冲激响应的阶数和相位参数,得到信号分解基,再根据信号分解原理得到通道数×阶数×相位数的三阶张量特征,并通过计算测试样本张量特征与训练样本张量特征间的相似性实现了水下无源声呐目标的分类识别。海上实录无源声呐目标的分类识别实验表明,提取的张量特征具有比较好的分类识别性能,听觉模型等效矩形带宽尺度优于线性尺度划分中心频率,能够提高无源声呐的目标指示能力。      

Tensor feature extraction of underwater passive sonar target based on auditory model

Feature extraction is a key step for underwater passive sonar target classification and recognition.A kind of tensor feature extraction method based on auditory PattersonHoldsworth cochlear model is proposed.First,the filter impulse response of the cochlear model is regarded as the basis function of signal decomposition,and the center frequency of different channels is determined according to the nonlinear scale or conventional linear scale of the auditory model.Then,the gain and bandwidth of the corresponding channel are calculated,and the order and phase parameters of the impulse response are quantified to obtain a relatively complete signal decomposition basis.And according to the principle of signal decomposition,the third-order tensor features of channel number-order number-phase number are obtained.Finally,the classification and recognition of the underwater passive sonar target is realized by calculating the similarity between the testing sample tensor feature and training sample tensor feature.The experiment on passive sonar target classification and recognition shows that the extracted tensor features have better classification and recognition performance,and the equivalent rectangular bandwidth scale of the auditory model is better than the linear scale to divide the center frequency,which can improve the target indication ability of passive sonar.     

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。     

Spatial smoothing for localized correlated sources – Its effect on different localization methods in the nearfield

The spatial smoothing (SS) technique has been proved to be effective in decorrelating coherent signals by restoring the rank of the signal covariance matrix R. Averaging the covariance matrices of subarrays of the original array, is a technique which increases the rank of the smoothed matrix R_SS. Algorithms like MUSIC or Capon, which rely on the use of the signal covariance matrix R and fail in the case of correlated sources, can be applied to scenarios with correlated sources after spatial smoothing.However, SS is most practically applied to uniformly spaced arrays or to arrays which have a translational symmetry.In addition the formulation is strictly applicable only to such farfield conditions, where the incoming waves are plane waves and the steering vectors to the sources of the different subarrays are identical. These conditions are not fulfilled in the nearfield.Spatial smoothing is now applied with an acoustic camera in the nearfield and it is shown that up to some limits this technique is applicable. Effects/limitations are studied using simulation and measurements with several Beamforming algorithms (MUSIC, Capon and Orthogonal Beamforming) are carried out.The results demonstrate the benefits of SS even in the nearfield up to some limits, which are given through the distance of the different subarrays in comparison to the spatial resolution of the Beamforming algorithm. Especially at lower frequencies SS in connection with MUSIC- or Capon-Beamforming give better resolution in comparison to D + S Beamforming.     

Experimental research for direction of arrival estimation based on signal phase matching principle

In order to test the validity of signal phase matching principle (SPMP) applied to direction of arrival (DOA) estimation, experiments are carried out at a reservoir using 16 sensors array. Two kinds of method, Least Square Method for Signal Matching principle (LSMSPM) and singular value decomposition method for signal matching principle (SVDSPM), are used for DOA estimation. Their performances were analyzed and compared with MUSIC and conventional beam-forming (CBF) method. The results show that the 3 dB beam width obtained by SPMP is 1/4 to 1/7 as much as that obtained by CBF and 1/2 to 1/3 by MUSIC method. In addition, LSMSPM and SVDSPM are available for multi-sources DOA estimation and high resolution DOA estimation, which demonstrates that DOA estimation by SPMP method is better than that by MUSIC and CBF method.     

Direction of arrival estimation and signal recovery based on single snapshot compressed sensing in frequency domain

Direction of arrival(DOA) estimation and signal recovery is the base of the underwater target localization,tracking and recognition.Based on the compressed sensing theory,a method for DOA estimation and source signal recovery is proposed using the single snapshot processing of the received array signal in frequency domain.The received array signal are transformed to frequency domain,and the single snapshot data in frequency domain are regarded as the measured data of the compressed sensing.According to the frequency,searching orientation and array manifold,the overcomplete array manifold is constructed as the sensing matrix of the compressed sensing.Both the target signal and power of the searching orientation are estimated by the basis pursuit method to complete DOA estimation and signal recovery.Simulation results show that the proposed method has a number of advantages over the minimum variance distortionless response(MVDR) method,including improved robustness to noise,fewer requirement in number of sensors and snapshots.And the correlation coefficient of the signal reaches up to 0.89.Experiment results in real environments verify that the proposed method performs more effectively in the detection of weak targets than the MVDR method and can be applied to real sonar system.