Vector-sensor array processing for electromagnetic sourcelocalization

The authors present a new approach for localizing electromagnetic sources using sensors where the output of each is a vector consisting of the complete six electric and magnetic field components. Two types of source transmissions are considered: (1) single signal transmission (SST), and (2) dual signal transmission (DST). The model is given in terms of several parameters, including the wave direction of arrival (DOA) and state of polarization. A compact expression is derived for the Cramer-Rao bound (CRB) on the estimation errors of these parameters for the multi-source multi-vector-sensor model. Quality measures including mean-square angular error (MSAE) and covariance of vector angular error (CVAE) are introduced, and their lower bounds are derived. The advantage of using vector sensors is highlighted by explicit evaluation of the MSAE and CVAE bounds for source localization with a single vector sensor. A simple algorithm for estimating the source DOA with this sensor is presented along with its statistical performance analysis     

Acoustic vector-sensor processing in the presence of a reflectingboundary

We consider the passive direction-of-arrival (DOA) estimation problem using arrays of acoustic vector sensors located in a fluid at or near a reflecting boundary. We formulate a general measurement model applicable to any planar surface, derive an expression for the Cramer-Rao bound (CRB) on the azimuth and elevation of a single source, and obtain a bound on the mean-square angular error (MSAE). We then examine two applications of great practical interest: hull-mounted and seabed arrays. For the former, we use three models for the hull: an ideal rigid surface for high frequency, an ideal pressure-release surface for low frequency, and a more complex, realistic layered model. For the seabed scenario, we model the ocean floor as an absorptive liquid layer. For each application, we use the CRB, MSAE bound, and beam patterns to quantify the advantages of using velocity and/or vector sensors instead of pressure sensors. For the hull-mounted application, we show that normal component velocity sensors overcome the well-known, low-frequency problem of small pressure signals without the need for an undesirable “stand-off” distance. For the seabed scenario, we also derive a fast wideband estimator of the source location using a single vector sensor     

水下电磁场源的二维DOA和极化估计

以电磁矢量传感器在均匀海水中的电声测量为运用背景,建立了水中单个电磁矢量传感器的数据接收模型,采用特征值分解的信号子空间TLS-ESPRIT算法,对基于该算法的单个电磁矢量传感器估计多个信号源的DOA和极化参数问题进行了研究。     

Two-Step Sparse Representation Based 2D DOA Estimation with Single Moving Acoustic Vector Sensor

This paper investigates the two dimensional direction of arrival (2D DOA) estimation problem of multiple sources with one single moving acoustic vector sensor (AVS). We first use one single moving AVS to construct a synthetic nested AVS array, which is later shown that is equivalent to the physical nested AVS array. Then the vectorization and row extraction operations are performed to obtain the observation vector that behaves like signals received by a virtual uniform AVS array. Finally, the 2D DOA estimation is obtained via a two-step sparse representation (SR) method, which transforms the 2D grid search to a computationally efficient 1D grid search. The Cramer-Rao bound comparison between the synthetic and physical nested AVS arrays shows that these two arrays are equivalent for DOA estimation. Based on the property of the nested arrays and the full utilization of the array aperture via SR, the proposed method can achieve better estimation performance than spatial smoothing methods with nested AVS arrays and methods with uniform AVS arrays. Simulations validate the effectiveness of the proposed synthetic array method.

     

声矢量阵远程定向技术(一)--新的协方差矩阵生成方法

MUSIC等子空间类DOA(direction of arrival)估计算法,以其较高的分辨能力和相对较小的计算量而颇受关注．但如果将其简单引用到声矢量阵中,将矢量传感器(AVS)的振速信息仅仅作为独立的阵元来处理,则并没有充分利用AVS 中声压和振速的相干性,以及由此带来的抗各向同性噪声能力．基于AVS中声压和振速的相干性原理,提出了一种新的声矢量阵协方差矩阵牛成方法．该方法完全利用了AVS的平均声强抗噪原理,具有较强的抗各向同性噪声能力,可将子空间类DOA估计方法与声矢量阵技术更为有效地结合起来,实现远程高分辩DOA估计．理论分析和基于湖试数据的仿真实验证明了新方法的有效性．     

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.     

Multilinear array manifold interpolation

Two algorithmic solutions for interpolating between array manifold grid points are presented. The algorithms are for sensor arrays in vector or in scalar wavefields. The algorithms make successful use of the condition that the reciprocal DOA (direction-of-arrival) spectrum is a multilinear function in the small, i.e. over the region of adjacent grid points. Since polarization is a linear parameter subspace, a DOA spectrum can be computed without recourse to the unknown polarizations. Then, vector interpolation is replaced by bivector interpolation to address the radio wavefield case. The approach distinguishes between sensor arrays in scalar wavefields (e.g. acoustic) and those in vector wavefields (e.g. electromagnetic). Since both storage and computational load vary with the reciprocal of the grid spacing or its square, the design relationship between desired accuracy and the required array manifold grid spacing is given     

Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework

This paper investigates the estimation of the two-dimensional direction of arrival (2D-DOA) of sound sources using an acoustic vector sensor array (AVSA) within a spatial sparse representation (SSR) framework (AVS-SSR-DOA). SSR-DOA estimation methods rely on a pre-defined grid of possible source DOAs and essentially suffer from the grid-effect problem: Reducing the size of the grid spacing leads to increased computational complexity. In this paper, we propose a two-step approach to tackle the grid-effect problem. Specifically, omnidirectional sensor array-based SSR-DOA estimation firstly provides initial low-cost DOA estimates using a coarse grid spacing. Secondly, a closed-form solution is derived by exploring the unique subarray manifold matrix correlation and subarray signal correlation of the AVSA, which allows for DOA estimates between the pre-defined angles of the grid and potentially achieves higher DOA estimation accuracy. To further alleviate the estimation bias due to noise and sparse representation model errors, line-fitting (LF) techniques and subspace techniques (ST) are employed to develop two novel DOA estimation algorithms, referred to as AVS-SSR-LF and AVS-SSR-ST, respectively. Extensive simulations validate the effectiveness of the proposed algorithms when estimating the DOAs of multiple sound sources. The proposed AVS-SSR-ST algorithm achieves high DOA estimation accuracy and is robust to various noise levels and source separation angles.     

三轴四元非典型声强向量阵对低频声源全空间定向的理论与实验研究

该文提出了应用3轴4元非典型声强向量阵在小尺度平台上对低频声源全空间定向的方法。该方法用四只传感器测量声强向量的3个正交分量,根据3个分量的几何关系来解出目标的方位。文中基于声信号的空间互谱分析,给出了声强向量阵的定向原理,并系统分析了其定向误差,包括有限差分误差、通道失配误差和环境噪声引起的误差。半消声室实验结果表明,在校正系统误差后,尺度为0.1m的非典型声强向量阵在各向同性噪声中的定向误差在1o左右,证实了该方法是可行的。     

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.     

基于信号相位匹配原理的二维方位估计方法

提出一种基于信号相位匹配原理的二维方位估计奇异值分解算法.通过对空间虚参考点选择的说明,推导出远场条件下的时延计算公式以及基于信号相位匹配原理的奇异值分解算法公式.通过分析基元间距、信号频率以及信噪比对定向精度的影响,证实了该算法在保持尖锐指向性的前提下,可以突破压差式矢量传感器对阵元间距的严格要求,加宽工作频带,并实现低信噪比条件下的高精度二维测向.仿真结果表明:在0.8λ的基元间距下,信噪比为0dB,FFT点数大于2560时,方位角、俯仰角估计的均方根误差小于1°.     

基于矢量传感器MIMO雷达的发射极化优化DOA估计算法

针对常规矢量传感器MIMO雷达没有利用发射极化信息导致波达方向(DOA)估计精度较差的问题,该文提出一种克拉美罗界(CRB)最小化的发射极化优化算法。首先建立矢量传感器MIMO雷达的接收信号模型;然后分析固定发射极化矢量传感器MIMO雷达DOA估计算法的不足;接着推导任意发射极化状态下的CRB,计算最小CRB对应的极化状态;最后利用该优化极化状态采用固定极化DOA估计算法得到DOA估计。该算法的DOA估计精度高于固定极化DOA估计算法。且该算法的2维DOA估计可自动配对,发射电磁矢量传感天线位置可任意。仿真结果证明了该算法的有效性。     

基于声能流矢量补偿的水下目标高精度DOA估计

针对各向异性噪声对水下目标方位估计精度产生严重干扰的问题,文中提出了一种基于声能流矢量补偿的水下目标高精度DOA估计方法。该方法基于声压和质点振速联合信息处理技术,在有效降低各向同性噪声影响的同时得到各向异性噪声源分布模型,并根据各向异性噪声场声能流模型对各向异性噪声进行矢量补偿,进一步实现了对各向异性噪声的抑制,达到高精度估计的目的。通过数值仿真对该方法的性能进行了验证。仿真结果表明,在20 dB以下,文中方法精度均高于常规复声强器DOA估计,精度最高提高了21%。     

相干声波信号DOA单快拍矢量平滑估计与跟踪算法

本文研究了基于声矢量传感器阵列的相干信号波达方向(DOA)估计和跟踪问题.首先,根据中心对称均匀线阵方向矩阵的平移不变特性,提出了一种增强阵列有效孔径的单快拍矢量平滑估计算法(PVFSIA),该算法可以用于相干信号的DOA快速估计.在此基础上,提出了基于迭代的相干信号DOA跟踪算法,该算法无需奇异值分解和矩阵求逆运算,...     

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。     

基于具有时序结构的稀疏贝叶斯学习的水声目标 DOA 估计研究

现有的基于CS-MMV（Compressed Sensing-Multiple Measurement Vectors）模型的DOA估计一般都假定信号源为独立同分布（ i．i．d）,算法建立在信号的空间结构上进行分析,而当处理具有时序结构的源信号时表现出性能和鲁棒性差的问题,为此该文提出一种具有时序结构的稀疏贝叶斯学习的DOA算法,该方法通过建立一阶自回归过程（ AR）来描述具有时序结构的水声信号,将信号源的时间结构特性充分应用到DOA估计模型中,然后采用针对多测量矢量的稀疏贝叶斯学习（ Muti-vectors Sparse Bayesian Learning ）算法重构信号空间谱,建立多重测量向量中恢复未知稀疏源的信号的CS（ Compressed Sensing ）模型,最终完成DOA估计．仿真结果表明该方法相对于传统的算法具有更高的空间分辨率和估计精度的特点,且抗干扰能力强．     

速度传感器阵列近场声源定位的孔径扩展算法

采用速度传感器阵列提出了一种近场声源定位(距离和DOA估计)的新算法.与目前提出的其它算法相比,本文算法有以下几个优点:无需计算高阶累积量,从而有较低的计算量;能够实现参数估计的自动配对;阵元间隔无需限制在1/4波长内,并可以通过增加阵元间隔扩展阵列孔径,从而提高算法的参数估计精度.论文最后给出了仿真实验,验证了算法的定位性能.     

线性电磁矢量阵列的空时极化平滑算法及相干源多参数估计

该文提出了一种基于线性电磁矢量阵列的空时极化平滑算法(STPSA),解决了相干信源的频率,2维波达方向和极化参数的联合估计问题。该算法通过对不同子阵和矢量传感器不同传感单元的测量数据及其相应延迟数据进行平滑,实现解相干预处理并抑制噪声干扰,然后利用传播算子方法得到相应的参数估计。与目前的算法相比,该文提出的算法能够同时实现相干信源的多个参数联合估计;无需通过奇异值或特征值分解提取信号/噪声子空间,也无须进行参数搜索,有较低的运算量;另外,算法能够通过增加相邻阵元的间隔来扩展阵列的有效孔径,改善估计性能,且无须进行参数去模糊处理。仿真结果验证了算法的有效性。     

Preprocessing for direction finding with minimal variancedegradation

Numerous authors have advocated the use of preprocessing in high-resolution direction of arrival (DOA) algorithms. The benefits cited include reduced computation, improved performance in spatially colored noise, and enhanced resolution. The authors identify the preprocessing matrices that provide minimum variance estimates of DOA for a number of models and algorithms. They examine the Cramer-Rao bound (CRB) for Gaussian signals, the CRB for deterministic signals, and the asymptotic variance of the MUSIC estimator for preprocessed data. They also study the effect of array manifold errors on the direction estimates. As expected, the optimal preprocessor requires knowledge of the source directions. However, they show that performance that is close to optimal can be obtained with only approximate knowledge of the source directions (with an error not exceeding the array beamwidth) if the design rules outlined in this paper are used     

Acoustic vector-sensor beamforming and Capon direction estimation

We examine the improvement attained by using acoustic vector-sensors for direction-of-arrival (DOA) estimation, instead of traditional pressure sensors, via optimal performance bounds and particular estimators. By examining the Cramer-Rao bound in the case of a single source, we show that a vector-sensor array's smaller estimation error is a result of two distinct phenomena: (1) an effective increase in signal-to-noise ratio due to a greater number of measurements of phase delays between sensors and (2) direct measurement of the DOA information contained in the structure of the velocity field due to the vector sensors' directional sensitivity. Separate analysis of these two phenomena allows us to determine the array size, array shape, and SNR conditions under which the use of a vector-sensor array is most advantageous and to quantify that advantage. By extending the beamforming and Capon (1969) direction estimators to vector-sensors, we find that the vector-sensors' directional sensitivity removes all bearing ambiguities. In particular, even simple structures such as linear arrays can determine both azimuth and elevation, and spatially undersampled regularly spaced arrays may be employed to increase the aperture and, hence, the performance. Large sample approximations to the mean-square error matrices of the estimators are derived and their validity is assessed by Monte Carlo simulation