基于扩展孔径波达方向矩阵法的高精度二维DOA估计

该文针对扩展孔径会产生估计模糊的现象,提出一种高精度扩展孔径波达方向矩阵算法。算法由个阵列单元组成双平行阵列几何结构,利用阵列传感器沿轴和沿轴的不同间距,分别构造波达方向矩阵,由此计算出高精度模糊的方向余弦估计和低精度无模糊的方向余弦估计,然后利用低精度无模糊的方向余弦估计值对高精度模糊的方向余弦估计值进行解模糊处理,得到高精度无模糊的方向余弦的估计值。该算法无需配对运算和2维搜索,是一种低运算量高精度的算法,计算机仿真实验验证了该算法的性能。     

一种毫米波干涉仪测向算法

提出了一种毫米波干涉仪二维测向解模糊算法.通过对圆形阵列阵元与参考阵元之间的测量相位差进行一次或多次虚拟变换运算,获得虚拟阵元与参考阵元之间的无模糊相位差.无模糊相位差被用来得到粗略无模糊的波达方向估计,此粗略无模糊的波达方向估计被用来解原阵列阵元和参考阵元的周期性相位模糊.进而获得高精度波达方向估计.仿真实验表明,所提算法在毫米波频率范围内能获得高精度的二维测向结果.     

一种毫米波干涉仪测向算法（英文）

提出了一种毫米波干涉仪二维测向解模糊算法.通过对圆形阵列阵元与参考阵元之间的测量相位差进行一次或多次虚拟变换运算,获得虚拟阵元与参考阵元之间的无模糊相位差.无模糊相位差被用来得到粗略无模糊的波达方向估计,此粗略无模糊的波达方向估计被用来解原阵列阵元和参考阵元的周期性相位模糊.进而获得高精度波达方向估计.仿真实验表明,所提算法在毫米波频率范围内能获得高精度的二维测向结果.     

一种基于余弦函数的相位干涉仪阵列DOA估计算法

该文针对相位干涉仪阵列估计波达角过程中的相位差模糊问题,提出一种基于余弦函数的波达角估计算法,并分析了该算法的估计值唯一性条件,该条件比相位差解模糊算法的唯一性条件更宽松。不同情况下的仿真结果表明,只要阵列的基线距离满足估计值唯一性条件,采用该算法能够有效进行高精度的波达角估计。     

单快拍数据的分布式阵列DOA估计

该文针对分布式阵列相干信号单次快拍波达方向估计问题,提出一种基于状态空间平衡法的1维波达角估计算法。该算法首先直接利用单快拍数据以分布式阵列每个子阵单元进行Hankle矩阵构造,然后采用状态空间平衡法,分别获得低精度无模糊的子阵单元内DOA估计和高精度有模糊的子阵单元间DOA估计,最后结合配对和解模糊算法获得高精度无模糊DOA估计。该算法不受信号形式限制,可同时对相干信号和非相干信号进行处理,能充分利用分布式阵列扩展阵列物理孔径特性,获得较高的DOA估计精度。计算机仿真结果验证了所提算法的有效性。     

基于双向传播算子的互质面阵二维波达方向估计

针对谱峰搜索的二维波达方向估计中现有算法复杂度高,精度受搜索间隔影响较大的问题,给出了一种双向传播算子的互质面阵二维波达方向估计算法,实现了俯仰角和方位角的低复杂、高精度、无模糊联合估计.该方法首先将互质阵列引入到二维波达方向估计中,构造互质平面阵模型,然后采用两次旋转不变传播算子方法计算出不同阵列流型方向上的旋转因子矩阵,根据旋转因子矩阵解算出目标信号的俯仰角和方位角,同时利用互质理论消除了稀疏阵列角度估计的不确定性,证明了互质阵列模型下采用双向传播算子方法进行俯仰角和方位角估计的无模糊性.对算法的复杂度进行理论分析,并给出了平面阵列角度估计的克拉美罗界推导.理论分析与仿真结果表明,算法不需要进行角度匹配和谱峰搜索,在相同条件下的均方根误差性能优于均匀平面阵的多重信号分类算法,并且以较低的复杂度无模糊的达到了高维网格搜索的精度.     

基于电磁矢量阵列孔径扩展方法的相干目标DOA估计

该文采用均匀且稀疏分布的电磁矢量矩形阵列,针对相干目标提出了一种有效的2维波达角(DOA)估计算法,该算法通过增加相邻阵元的间隔来扩展阵列的有效孔径,从而提高算法的DOA估计性能。论文首先结合极化平滑算法和传播算子方法得到存在相位周期性模糊的方向余弦估计。为了解决模糊性问题,论文通过协方差矩阵平滑提出一种新的解相干预处理算法,由该算法得到的信号子空间包含矢量阵元的导向矢量,且不存在相位模糊,利用此特点实现去模糊处理,得到目标的DOA估计。仿真结果表明,与基于ESPRIT的孔径扩展算法相比,提出的算法能够实现相干目标的DOA估计,同时无需特征值或奇异值分解,有更低的运算量。     

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。     

分离式长电偶极子稀疏阵列的相干信号多维参数联合估计

由长电偶极子或大磁环组成的大尺寸电磁矢量传感器(ElectroMagnetic Vector Sensor,EMVS)比小尺寸EMVS辐射效率更高,研究其参数估计算法有助于推动EMVS的实装化应用.该文针对分离式长电偶极子稀疏阵列相干目标参数估计问题,提出高精度无模糊的多维参数闭式解算法.首先利用空域旋转不变性和单个矢量传感器内部属性得到方向余弦的高精度周期性模糊估计值,然后借助单个矢量传感器导向矢量推导出2维波达方向粗估计值,最后通过解模糊得到高精度无模糊的多维参数估计值.该方法规避了传统极化平滑算法的极化信息损失和迭代搜索过程,且能实现参数自动配对.计算机仿真结果表明了所提算法在分离式长电偶极子线阵中解相干的有效性.     

基于传播算子的声学矢量传感器阵列 扩展孔径二维DOA估计算法

 本文提出一种基于传播算子的声学矢量传感器阵列扩展孔径二维DOA估计算法.首先,利用传播算子方法得到一组高精度模糊的DOA估计值;然后,利用声学矢量传感器的特点得到另一组低精度无模糊的DOA估计值;最后,利用无模糊估计值对模糊估计值进行解模糊处理,得到高精度无模糊的DOA估计值.提出的算法无需进行特征值分解或奇异值分解进行信号子空间/噪声子空间的估计.与基于ESPRIT的算法相比,提出的算法的计算量约为信号个数与声学矢量传感器个数的四倍之比.计算机仿真结果表明在信噪比不是很低时,提出的算法与基于ESPRIT的算法具有相当的估计性能.     

Computationally efficient 2-D DOA estimation for non-uniform two-L-shaped array

A two-dimensional direction-of-arrival (DOA) estimation method for non-uniform two-L-shaped array is presented in which the element spacing is larger than half-wavelength. To extract automatically paired low-variance cyclically ambiguous direction cosines and high-variance unambiguous direction cosines from the sub-blocks, the proposed method constructs and partitions the cross-correlation matrices. Then, the low-variance unambiguous direction cosines are obtained using the ambiguity resolved technique. Simulation results demonstrate that the proposed method has lower computation complexity and higher resolution than the existing methods especially when the elevation angles are between 70 and 90 degrees.     

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.     

Efficient 2-D DOA estimation for coherent sources with a sparse acoustic vector-sensor array

This paper proposes a computationally efficient two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithm based extended-aperture for acoustic coherent signals impinging on a sparse acoustic vector-sensor array. The coherency of incident signals is decorrelated through matrix averaging and the signal/noise subspaces are reconstructed through a linear operation of a matrix formed from the cross-correlations between some sensor data, where the effect of additive noise is eliminated. Consequently, DOAs can be estimated without performing eigen-decomposition (into signal/noise subspaces), and there is no need to evaluate all correlations of the array data. The derived estimates are automatically matched by translating eigenvalues into real-valued ones, furthermore, the proposed method can achieve the unambiguous direction estimates with enhanced accuracy by setting the vector sensors to space much farther apart than a half-wavelength, and it is also suitable for the case of spatially nonuniform noise, which may be more realistic scenario for the sparsely placed sensors. The performance of the proposed method is demonstrated through numerical examples.     

2-D DOA estimation in case of unknown mutual coupling for multipath signals

In this paper, two dimensional (2-D) direction-of-arrival (DOA) estimation problem in case of unknown mutual coupling and multipath signals is investigated for antenna arrays. A new technique is proposed which uses a special array structure consisting of parallel uniform linear array (PULA). PULA structure is complemented with auxiliary antennas in order to have a structured mutual coupling matrix (MCM). MCM has a symmetric banded Toeplitz structure which allows the application of the ESPRIT algorithm for 2-D paired DOA estimation. The advantage of the PULA structure is exploited by dividing it into overlapping linear sub-arrays (triplets) and spatial smoothing is employed to mitigate multipath signals. Closed form expressions are presented for search-free, paired and unambiguous 2-D DOA estimation. Two algorithms PULA-1 and PULA-2 are proposed to effectively solve the problem. Several simulations are done and the accuracy of the proposed solution is shown.     

基于均匀圆阵的近场源三维参数估计

基于均匀圆阵,提出一种近场源距离-方位角-俯仰角联合估计算法。利用阵元观测数据,构造一组高阶累积量矩阵,通过矩阵联合对角化技术得到阵列流形矩阵的估计。根据阵列流形矩阵的估计以及近场和远场条件下方位角相同的结论,获得方位角的估计。利用阵列流形矩阵和方位角的估计,得到距离和俯仰角的估计。该方法无需二维频域峰值搜索或参数配对。计算机仿真验证了算法的有效性。     

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.     

Efficient 2-D DOA and frequency estimation for L-shaped array via RD-PM

This paper addresses the issue of joint two-dimensional direction of arrival (2-D DOA) and frequency estimation via reduced-dimensional propagator method (RD-PM) with L-shaped array. The proposed algorithm has no need for eigenvalue decomposition of the sample covariance matrix and simplifies three-dimensional global spectral search within the three-dimensional propagator method (3-D PM) to one-dimensional local search, which greatly reduces computational complexity. Furthermore, the proposed algorithm can work under both uniform and non-uniform L-shaped array and can achieve paired 2-D DOA and frequency estimates automatically. In addition, the 2-D DOA and frequency estimation performance for the proposed method is approximate 3-D PM algorithm and parallel factor (PARAFAC) method but exceeds the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and improved PM algorithm. The detailed derivation of Cram´er-Rao bound (CRB) is provided and the simulation results demonstrate the effectiveness and superiority of the proposed approach.