Subspace extension algorithm for 2D DOA estimation with L-shaped sparse array

A subspace extension algorithm for two-dimensional (2D) direction-of-arrival (DOA) estimation with an L-shaped array is proposed. This L-shaped array is comprised of two orthogonal sparse linear arrays (SLAs). Each SLA consists of two different uniform linear arrays. The cross-correlation matrix of received data is used to construct two extended signal subspaces, by which the azimuth angles and elevation angles can be estimated independently. The procedure used to extend signal subspace only needs a small amount of calculation. Then, an effective pair-matching method is addressed to pair the estimated elevation angles and azimuth angles. Although the signal subspaces are extended, the complexity of the proposed 2D DOA estimation algorithm is lower than many similar algorithms. Simulation results indicate the availability of the proposed pairing-matching method and subspace extension algorithm.     

单快拍数据的分布式二维阵列测角方法研究

该文针对2维阵列波达方向估计问题,提出一种基于单快拍数据的分布式2维DOA估计算法。该算法首先利用每个子阵单元的单快拍数据进行2维Hankle矩阵构造;然后基于2维状态空间平衡法分别获得方位角和俯仰角子阵单元内DOA估计与子阵单元间DOA估计;最后通过解模糊算法获得方位角和俯仰角高精度无模糊DOA估计。该算法较好地解决了子阵单元内DOA估计和子阵单元间DOA估计之间的配对问题以及俯仰角和方位角之间配对问题,充分利用分布式阵列扩展阵列物理孔径特性;同时该算法可直接对相干信号和非相干信号进行处理。计算机仿真结果验证了所提算法的有效性。     

一种自动匹配的分布式非圆信号二维DOA快速估计方法

在相干分布式非圆信号2维波达方向(DOA)估计中,针对利用非圆特性后维数扩展带来的较大复杂度问题,且现有的低复杂度算法均需要额外的参数匹配,该文提出一种基于互相关传播算子的自动匹配2维DOA快速估计算法。该算法考虑L型阵列,在建立相干分布式非圆信号扩展阵列模型的基础上,首先证明了L阵中两个子阵的广义方向矢量(GSV)均具有近似旋转不变特性,然后通过阵列输出信号的互相关运算消除了额外噪声,最终利用子阵GSV的近似旋转不变关系通过传播算子方法得到中心方位角与俯仰角估计。理论分析和仿真实验表明,所提算法无须谱峰搜索和协方差矩阵特征分解运算,具有较低的计算复杂度,并且能够实现2维DOA估计的自动匹配;同时,相比于现有的相干分布式非圆信号传播算子算法,所提算法以较小的复杂度代价获得了性能的较大提升。     

Multiple Emitters Localization by UAV with Nested Linear Array: System Scheme and 2D-DOA Estimation Algorithm

Unmanned Aerial Vehicle(UAV)equipped with uniform linear array has been applied to multiple emitters localization.Meanwhile,nested linear array enables to enhance localization resolution and achieve under-determined Direction of Arrival(DOA)estimation.In this paper,we propose a new system structure for emitters localization that combines the UAV with nested linear array,which is capable of significantly increasing the positioning accuracy of interested targets.Specifically,a localization scheme is designed to obtain the paired two-dimensional DOA(2D-DOA,i.e.azimuth and elevation angles)estimates of emitters by nested linear array with UAV.Furthermore,we propose an improved DOA estimation algorithm for emitters localization that utilizes Discrete Fourier Transform(DFT)method to obtain coarse DOA estimates,subsequently,achieve the fine DOA estimates by sparse representation.The proposed algorithm has lower computational complexity because the coarse DOA estimates enable to shrink the range of over-complete dictionary of sparse representation.In addition,compared to traditional uniform linear array,improved 2D-DOA estimation performance of emitters can be obtained with a nested linear array.Extensive simulation results testify the effectiveness of the proposed method.     

一种稀疏表示的二维DOA降维估计新算法

提出了一种基于空间锥角降维的二维DOA稀疏分解估计新方法,解决了利用稀疏表示方法进行二维DOA估计时计算复杂度大的问题.根据L阵列的结构特性,引入空间锥角表示信号的二维DOA信息,构造空间锥角冗余字典,通过稀疏正则化求解实现空间锥角的估计,然后利用求解得到功率实现L阵列中两个子阵之间的空间锥角配对,从而达到对多来波的二维DOA估计的目的,其避免了方位角和俯仰角组合而造成冗余字典庞大的问题,极大地减少了稀疏分解的计算量.仿真和实测数据结果均验证了该方法的有效性和优越性,为进一步的工程应用奠定了基础.     

基于虚拟阵列的二维稀疏阵列相干源DOA估计

波达方向DOA估计是雷达阵列信号处理的一个重要方向,传统的MUSIC算法对均匀阵列条件下独立信号源的估计有很强的适应性,但实际的雷达工作中,稀疏布阵下多相干源测角是一个经常出现的应用场景。文中针对二维稀疏阵列的相干源测角,提出了一种基于虚拟阵列的相干源DOA估计方法。该方法利用虚拟阵列内插的方法,将一个任意二维稀疏阵列内插为一个均匀面阵,再通过二维空间平滑方法对相干源进行测角,能够同时获得信号的方位角和俯仰角信息。稀疏面阵和稀疏圆阵的仿真实验结果表明,该方法可以有效的解决二维稀疏阵列的相干源测角问题。     

基于MUSIC算法的L型阵列MIMO雷达降维DOA估计

该文提出一种基于MUSIC算法的L型阵列多输入多输出雷达降维波达方向(DOA)估计算法。该算法首先针对L型阵列导向矢量的结构,构造出一个降维矩阵,将回波信号转换到低维空间。然后利用二次优化方法将2维DOA估计分解为两个1维DOA估计。最后利用MUSIC空间谱估计其中1维角度,并利用求得的角度回代谱函数,对另1维角度进行求根估计。该算法将2维空间谱搜索降为1维搜索,极大地降低了运算复杂度。理论分析和仿真结果验证了该算法的准确性和可行性。     

一种可自动配对的二维波达方向估计算法

针对非相关远场窄带信号,提出了一种基于L阵模型的低复杂度高分辨二维波达方向估计算法。该方法通过利用互相关矩阵以及L阵的几何结构,消除了附加噪声的影响,且对于方位角和俯仰角的求取仅仅需要一维估计,同时该算法只需对一个低维矩阵进行特征值分解,减少了运算负担,该算法利用特征值求取俯仰角,利用相应的特征向量构造出的虚拟阵列响应矩阵求取与俯仰角配对的方位角。仿真进一步证明了算法的有效性,通过仿真还可以看出,该文所提方法在低信噪比、小快拍数和较小角度间距的情况下都具有良好的性能。     

L-shaped array-based elevation and azimuth direction finding in the presence of mutual coupling

For two-dimensional (2-D) directions-of-arrival (DOA) estimation problem, both the mutual coupling and the failure in pairing can cause severe performance degradation. In this paper, a new elevation and azimuth direction finding algorithm is developed to overcome the above-mentioned two difficulties in the L-shaped array configuration. The key points of this paper are: (i) constructing several correlation matrices to blindly compensate the effect of unknown mutual coupling using the outputs of properly chosen sensors and (ii) deriving a rank-reduction propagator method to estimate elevation and azimuth angles so as to avoid pairing parameters. Simulation results are presented to validate the performance of the proposed method.     

采用降秩多级维纳滤波器的二维DOA估计快速算法

针对双平行线阵的二维波达方向(DOA)估计问题,为有效降低计算复杂度,提出了一种基于降秩多级维纳滤波器(MSWF)的快速算法。首先利用MSWF的前向递推实现信号子空间的快速估计,无需估计协方差矩阵和特征分解;然后,通过MUSIC算法对方位角和俯仰角的估计进行分维估计,使二维DOA估计退化为两个一维DOA估计问题,且方位角和俯仰角自动配对,进一步降低了运算量。仿真结果表明,该方法的估计精度优于同样基于双平行线阵提出的波达方向矩阵法(DOAM),俯仰角兼并时同样适用,计算复杂度低,适用于实时性要求高的应用背景。     

基于主奇异矢量的L型阵列相干信号二维DOA估计方法

针对现有L型阵列相干信号DOA估计算法精度不高、孔径损失较大的问题,该文提出一种基于主奇异矢量的解相干(L-PUMA)方法以及改进的主奇异矢量法(L-MPUMA)。L-PUMA算法首先对互协方差矩阵进行降噪,再通过奇异值分解得到2维主奇异矢量,然后利用加权最小二乘法得到线性预测方程的多项式系数,该线性预测方程的根即为信号的DOA估计,最后提出一种新的配对算法实现仰角和方位角的配对。L-MPUMA算法利用反向共轭变换构造增广主奇异矢量,进一步提高了数据利用率,克服了信号完全相干时L-PUMA算法性能下降严重的问题,仿真实验验证了所提算法的高效性。     

Two-dimensional DOA estimation for acoustic vector-sensor array using a successive MUSIC

This paper discusses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for acoustic vector-sensor array, and derives a successive multiple signal classification (MUSIC) algorithm therein. The proposed algorithm obtains initial estimations of the azimuth and elevation angles obtained from the signal subspace, and uses successively one-dimensional local searches to achieve the joint estimation of 2D-DOA. The proposed algorithm, which requires the one-dimension local searches, can avoid the high computational cost within 2D-MUSIC algorithm. The proposed algorithm can obtain automatically-paired 2D-DOA estimation for acoustic vector-sensor array, and it has better DOA estimation performance than propagator method, estimation of signal parameters via rotational invariance technique algorithm and trilinear decomposition algorithm. Meanwhile, it has very close angle estimation to 2D-MUSIC algorithm. Furthermore, it is suitable for non-uniform linear arrays, works well for the sources with the same azimuth angle, and imposes less constraint on the sensor spacing, which does not have to be restricted within half-wavelength. We have also derived the mean-square error of DOA estimation of the proposed algorithm and the Cramer-Rao bound of DOA estimation. Simulation results verify the usefulness of the proposed algorithm.     

稀疏表示框架下无需参数配对的二维到达角估计

现有二维到达角估计算法大多基于子空间理论及需要参数配对,针对这一问题,在稀疏表示理论框架下提出了一种参数自动配对的二维到达角估计新算法。该算法在L阵列下构建阵列互相关矩阵的稀疏表示模型,利用奇异值分解降低复杂度并基于群LASSO(Least Absolute Shrinkage and Selection Operator)获得方位角估计。在方位角估计的基础上,基于向量化操作构建稀疏空间谱匹配模型,然后利用LASSO 获得俯仰角估计。与参数配对ESPRIT 和改进的传播算子方法相比,所提算法不仅无需参数配对过程,而且可以提供改进的估计精度。计算机仿真结果验证了所提算法的有效性。     

四元数在二维波达方向估计中的应用

基于四元数,本文提出了一种针对双平行阵的二维波达方向估计方法。与复数不同,四元数是一种维数更高的多元数代数。利用四元数代数理论,波达方向估计问题可以以高维的角度来求解。本文将四元数的概念引入到双平行阵接收模型中,建立了双平行阵的四元数接收模型。所提算法利用四元数的不同基之间的共同特性,对四元数子空间与信号方向矢量的正交关系进行解耦,得到仅含方位角信息的方向矢量与四元数子空间的正交表达式,并通过一维搜索估计出方位角。根据得到的方位角,算法进一步估计得到信源的俯仰角。仿真实验验证了本文所提算法的有效性。      

一种新的二维角度估计的高分辨算法

该文针对常规2维波达方向估计的高分辨算法运算量大和稳健性差等问题,提出了一种新的2维角度估计的高分辨方法。该方法首先建立基于范数约束的最优化问题的目标函数;然后用迭代算法沿均匀面阵接收数据的方位向求最小化目标函数的稀疏解,得到方位、俯仰角耦合的空间角频率,并分离信号;最后对每个分离的信号,沿面阵俯仰向求稀疏解,得到信号的俯仰角,进而求得对应的方位角。针对算法存在测角盲区的问题,提出了一种改进方法,通过求解空间2维稀疏解得到信号的2维角度。与传统的高分辨算法相比,该方法对信噪比和快拍数要求不高、无需特征值分解和多维搜索过程,具有较高的分辨力和极低的旁瓣电平。     

基于ADMM的低仰角目标二维DOA估计算法

针对面阵米波(VHF)雷达低仰角目标2维DOA估计问题,该文提出一种基于交替乘子法(ADMM)的快速2维DOA估计算法。该方法首先利用均匀面阵条件下方位、俯仰角无耦合的特性,将2维角度估计问题转化为两个1维角度估计问题,通过方位、俯仰维波束合成实现对目标信息提取;其次根据信号模型建立信号空域超完备表达式,利用ADMM方法完成对方位、俯仰角估计。该方法避免了2维联合估计复杂计算量,复杂度大大降低,且运算过程无需特征分解,进一步提高了运算效率。仿真结果表明了该算法的优越性。     

Two-dimensional direction-of-arrival estimation and pairing using L-shaped arrays

This paper proposes a computationally efficient azimuth and elevation estimation and pairing method using L-shaped uniform arrays. The azimuths and elevations of the incident signals are estimated independently at first using the outputs of the two array arms via equation rooting, which well avoids the computationally demanding spatial scanning procedure contained in most of the previous direction-of-arrival estimation methods. The order of the equations equals the number of the incident signals; thus, this procedure is computationally very cheap and can be implemented using various numeric algorithms. Then two optional methods are proposed for azimuth–elevation pairing. One method exploits the cross-correlation of the two subarray outputs of the L-shaped array, and the other method is realized by estimating the signal powers based on the direction estimates. Both of the two direction-pairing methods are implemented using numerical computations; thus, this procedure is also computationally very cheap. In-depth analyses are provided on the selection of the two optional azimuth–elevation pairing methods in different environments. Numerical examples are carried out to demonstrate the performance of the proposed method.     

基于Toeplitz矩阵重构的相干信源二维DOA估计算法

针对空间相干信源的二维DOA估计问题,提出一种基于Toeplitz矩阵重构的新算法。采用垂直放置的L形阵列,通过对重构的Toeplitz矩阵进行特征分解,得到对应的噪声子空间,并用MUSIC算法进行有限次一维搜索,先后估计出俯仰角和相应的方位角。与空间平滑处理算法相比,不减少阵元的有效孔径,具有更强解相干能力,估计性能也优于后者。最后计算机仿真结果证实了算法的有效性和可行性。     

基于矩阵差分的远场和近场混合源定位方法

混合源定位在无源雷达中发挥着重要作用.针对均匀圆阵下基于相位差方法的定位精度较低的问题,该文提出基于矩阵差分的远场和近场混合源定位方法.首先,利用二维多重信号(2-D MUSIC)分类方法估计出远场源的方位角和俯仰角;随后,利用协方差矩阵差分方法提取出近场源差分矩阵,通过改进的类旋转不变估计信号参数(ESPRIT-li...     

时空欠采样宽频段信号频率和二维角估计方法

本文提出了一种时空欠采样宽频段(2~18GHz)信号频率、方位角和仰角三维参数估计方法.该方法采用均匀圆阵和阵元延时,可在时间欠采样条件下实现频率无模糊估计;在空间欠采样条件下,用整数搜索法实现方位角和仰角无模糊估计,且频率、方位角和仰角估计可自动配对.该方法不仅可用于二阶统计量阵元空间和DFT波束空间,而且还可用于四阶累积量,可在高斯白或色噪声背景下,估计非高斯信源的参数.在DFT波束空间、阵元数相同和最大孔径相当时,该方法方位角和仰角估计的方差比L阵方法的方差小1~2个数量级.仿真实验表明了算法的有效性.