基于盲波束形成的分布式目标波达方向估计方法

在分布式目标波达方向估计问题中,空间信号分布函数一般具有共轭对称性.本文提出了一种新的盲波束形成方法,不用进行多维参数搜索,不要求已知空间信号分布的具体函数形式,并适用于分布函数形式不同的分布式目标同时存在的情况.仿真实验结果表明,这种方法的波达方向估计性能对信号源的分布特性不敏感.     

Source localization using recursively applied and projected (RAP)MUSIC

A new method for source localization is described that is based on a modification of the well-known MUSIC algorithm. In classical MUSIC, the array manifold vector is projected onto an estimate of the signal subspace. Errors in the estimate of the signal subspace can make localization of multiple sources difficult. Recursively applied and projected (RAP) MUSIC uses each successively located source to form an intermediate array gain matrix and projects both the array manifold and the signal subspace estimate into its orthogonal complement. The MUSIC projection to find the next source is then performed in this reduced subspace. Special assumptions about the array manifold structure, such as Vandermonde or shift invariance, are not required. Using the metric of principal angles, we describe a general form of the RAP-MUSIC algorithm for the case of diversely polarized sources. Through a uniform linear array simulation with two highly correlated sources, we demonstrate the improved Monte Carlo error performance of RAP-MUSIC relative to MUSIC and two other sequential subspace methods: S and IES-MUSIC. We then demonstrate the more general utility of this algorithm for multidimensional array manifolds in a magnetoencephalography (MEG) source localization simulation     

混合信源波达方向估计算法

本文针对非相干混合点信源和分布式信源,提出了一种基于对称均匀线阵的波达方向估计算法。该算法利用点信源和分布式信源协方差矩阵结构的不同,采用空间差分技术将两种信源分离。对于点信源,采用传统MUSIC算法估计其波达方向;对于分布式信源,利用信号子空间的旋转不变性来估计其波达方向。该算法不仅消除了点信源对分布式信源的影响,也无需估计分布参数,大大降低了计算复杂度。且采用2N+1个阵元的对称均匀线阵可估计出 2N 个混合信源,其中分布式信源最多为 N 个,有效减小了阵列的孔径损失。仿真结果表明该算法的性能优于广义特征值分解的算法。      

基于遗传算法的水下阵列DOA估计研究

利用遗传算法研究水下阵列对目标的方位角估计问题,提出了一种新的方法证明遗传算法中的适应度函数对应于信号源的阵列流型取得极大值,采用该适应度函数的遗传算法(GA)计算量介于MUSIC算法和DML算法之间.通过计算机仿真验证,采用该适应度函数的遗传算法进行的DOA估计具有分辨相干目标的能力,角度分辨力高于MUSIC算法,估计偏差和均方误差在中低信噪比条件下小于MUSIC算法,与DML算法基本一致.     

基于PSO-MUSIC算法的分布式目标DOA估计

提出了一种基于粒子群优化算法（PSO）和多重信号分类（MUSIC）算法的分布式目标波达方向（Direction of Arrival,DOA）估计方法。在空间欠采样情况下,该方法首先利用粒子群优化算法优化阵列阵元间距,得到阵列天线方向图高旁瓣电平最小情况下的阵元间距,阵列阵元间距决定了阵列流形,然后在该阵列流形下构造分布式目标信号模型,最后结合分布式目标导向矢量和MUSIC算法获得空间欠采样情况下分布式目标中心DOA的准确估计。仿真结果表明了该方法的有效性。     

MUSIC及其改进算法的研究与实现

研究了用于阵列信号处理领域的重要分支,即信号到达角估计的几种MUSIC算法。阐述了经典MUSIC算法、前后向空间平滑MUSIC算法、改进MUSIC算法的原理。采用Matlab进行的仿真表明,经典MUSIC算法对于非相干信源的到达角估计具有良好的性能,但不能用于相干信源的到达角估计;前后向空间平滑MUSIC算法使可估计信源数减小、计算复杂度较高;改进MUSIC算法性能最好,不影响非相关信源的估计,也无明显计算量增加。几种算法在到达角估计中有各自的优缺点。     

Point-source localization in blurred images by a frequency-domaineigenvector-based method

We address the problem of resolving and localizing blurred point sources in intensity images. Telescopic star-field images blurred by atmospheric turbulence or optical aberrations are typical examples of this class of images, a new approach to image restoration is introduced, which is a generalization of 2-D sensor array processing techniques originating from the field of direction of arrival estimation (DOA). It is shown that in the frequency domain, blurred point source images can be modeled with a structure analogous to the response of linear sensor arrays to coherent signal sources. Thus, the problem may be cast into the form of DOA estimation, and eigenvector based subspace decomposition algorithms, such as MUSIC, may be adapted to search for these point sources. For deterministic point images the signal subspace is degenerate, with rank one, so rank enhancement techniques are required before MUSIC or related algorithms may be used. The presence of blur prohibits the use of existing rank enhancement methods. A generalized array smoothing method is introduced for rank enhancement in the presence of blur, and to regularize the ill posed nature of the image restoration. The new algorithm achieves inter-pixel super-resolution and is computationally efficient. Examples of star image deblurring using the algorithm are presented.     

Spatial polarimetric time-frequency distributions for direction-of-arrival estimations

Time-frequency distributions (TFDs) are traditionally applied to a single antenna receiver with a single polarization. Recently, spatial time-frequency distributions (STFDs) have been developed for receivers with multiple single-polarized antennas and successfully applied for direction-of-arrival (DOA) estimation of nonstationary signals. In this paper, we consider dual-polarized antenna arrays and extend the STFD to utilize the source polarization properties. The spatial polarimetric time-frequency distributions (SPTFDs) are introduced as a platform for processing polarized nonstationary signals, which are received by an array of dual-polarized double-feed antennas. This paper deals with narrow-band far-field point sources that lie in the plane of the receiver array. The source signals are decomposed into two orthogonal polarization components, such as vertical and horizontal. The ability to incorporate signal polarization empowers the STFDs with an additional degree of freedom, leading to improved signal and noise subspace estimates for direction finding. The polarimetric time-frequency MUSIC (PTF-MUSIC) method for DOA estimation based on the SPTFD platform is developed and shown to outperform the time-frequency, polarimetric, and conventional MUSIC techniques, when applied separately.     

一种基于逐次搜索的快速MUSIC方法

MUSIC方法是一种经典的阵列波达方向(DOA)估计子空间方法,在高信噪比时,该方法有很好的性能,但随信噪比的降低,算法性能下降很快,特别是其分辨概率下降很快。其主要原因是低信噪比和DOA相邻较近时,信号源之间的空间相关性较强。逐次消除空间相关性并逐次搜索DOA的改进MUSIC方法,提高了分辨概率。为降低运算量,采用多级维纳滤波代替特征值分解过程。文中方法具有一般性,适合于基于子空间方法的一大类DOA估计。仿真结果证明了方法的有效性。     

Receiver array calibration using disparate sources

We present a new array calibration procedure for over-the-horizon (OTH) radar, using disparate sources. Unlike previous array calibration methods, which require a specific type or class of sources for calibrating the array, the method we propose can use combinations of single-mode, multimode, and near-field sources; each source with either known or unknown DOAs (directions-of-arrival). Multidimensional MUSIC is exploited for time-invariant DOA sources, while single-snapshot techniques are used for sources that have time-varying DOAs. A nonlinear separable least-squares solution to the array calibration problem is used to estimate the array coupling matrix and sensor positions. Simulation results indicate that good estimates are obtained for the unknown parameters and further the array sidelobe levels and bearing errors are significantly reduced when these estimated parameters are used in array processing. The algorithm performance was also compared with the Cramer-Rao lower bound and found to be statistically efficient     

空间任意阵的多宽带信源定位算法性能研究

针对宽带信号非平稳特性,以及常用宽带DOA估计算法要求信源个数是已知的情况,提出一种新的基于空间任意阵的非平稳信号DOA估计算法。首先,通过短时傅里叶变换将信号转为频域表示,然后构造阵列频域数据模型,最后利用短时功率谱矩阵的联合对角化特性实现宽带信号的波达方向估计。本文对提出的算法进行了理论分析,并在常见阵型（如线阵、十字阵）上进行了仿真和性能分析,仿真结果表明该算法可高分辨率地估计出DOA,并且估计误差小。相对于已有的MUSIC谱估计方法,该算法无需进行信源个数估计,更具实用性。      

Direction of arrival estimation for more correlated sources than active sensors

In this paper, a new direction of arrival (DOA) estimation method for more correlated sources than active receiving antennas is proposed. The trick to solve this problem using only second-order statistics is to consider a periodic scanning of an underlying uniform array, where a single scanning period contains several time slots and in different time slots different sets of antennas are activated leading to a dynamic non-uniform array with possibly less active antennas than sources in each time slot. We collect the spatial correlation matrices of the active antenna arrays for all time slots and are able to present them as a linear function of the spatial correlation matrix of the underlying array. We provide a necessary and sufficient condition for this system of equations to be full column-rank, which allows for a least squares (LS) reconstruction of the spatial correlation matrix of the underlying array. Some practical greedy algorithms are presented to design dynamic arrays satisfying this condition. In a second step, we use the resulting spatial correlation matrix of the underlying array to estimate the DOAs of the possibly correlated sources by spatial smoothing and MUSIC. Alternatively, we can express this matrix as a linear function of the correlation matrix of the sources (incoming signals) at a grid of investigated angles, and solve this system of equations using either LS or sparsity-regularized LS (possibly assisted by additional constraints), depending on the grid resolution compared to the number of antennas of the underlying array.     

SWEDE算法提高模式空间内DOA估计抗噪声能力

均匀测向圆阵相干信号DOA估计在模式空间内将阵列流形化为线性结构后 ,一般情况下都是先进行空间平滑去相干处理后再运用MUSIC算法进行DOA估计 ,文中运用SWEDE算法代替MUSIC算法进行了模式空间内的DOA估计 ,仿真结果证明SWEDE算法更好地适应了均匀圆阵模式空间内DOA估计数据矩阵的特点 ,使系统抗噪声能力、测向精度、分辨率均得到较大提高     

Passive localization of near-field sources by path following

A new algorithm for passively estimating the ranges and bearings of multiple narrow-band sources using a uniform linear sensor array is presented. The algorithm is computationally efficient and converges globally. It minimizes the MUSIC cost function subject to geometrical constraints imposed by the curvature of the received wavefronts. The estimation problem is reduced to one of solving a set of two coupled 2D polynomial equations. The proposed algorithm solves this nonlinear problem using a modification of the path-following (or homotopy) method. For an array having m sensors, the algorithm reduces the global 2D search over range and bearing to 2(m-1) independent 1D searches. This imparts a high degree of parallelism that can be exploited to obtain source location estimates very efficiently     

Joint azimuth, elevation, and time of arrival estimation of diffuse sources

In this paper, we estimate the azimuth, the elevation, and the time of arrival of diffuse sources using the covariance matching estimator (COMET) algorithm. Previous works dealt with azimuth estimation of diffuse sources or azimuth and time of arrival estimation of point sources. However, in realistic situations, a tridimensional diffuse source localization is needed, which is the main objective of this paper. We show that the dimensionality of the COMET algorithm can be reduced by separating the estimation of the different source powers and the noise variance from that of the remaining parameters, namely the azimuth, the elevation, the time of arrival, and the corresponding angular and temporal spreads. As COMET still involves a multidimensional nonlinear optimization, we choose, in this purpose, the alternating projection algorithm to alleviate the corresponding complexity. The multiple signal classification (MUSIC) algorithm is processed to initialize the so-resulted algorithm. Simulations of the proposed algorithm are carried in different contexts and compared to the Cramér-Rao Bound, MUSIC algorithm, and dispersed signal parametric estimation simulation results.     

一种分布式目标波达方向估计方法

本文提出一种在多径情况下,利用广义阵列流形的特点,通过求解矩阵的广义特征值分解,不用进行谱峰搜索或多维参数搜索,直接由广义特征值得到分布式目标波达方向（DOA）估计的方法。该方法的估计性能对信号源的分布特性优于MUSIC方法。     

Robustness of the coherently distributed MUSIC algorithm to the imperfect knowledge of the spatial distribution of the sources

The MUltiple SIgnal Classification (MUSIC) estimator has been widely studied for a long time for its high resolution capabilities in the domain of the directional of arrival (DOA) estimation, with the sources assumed to be point. However, when the actual sources are spatially distributed with angular dispersion, the performance of the conventional MUSIC is degraded. This paper deals with the sensitivity of MUSIC to modeling error due to coherently distributed (CD) sources. A performance analysis of an extended MUSIC taking into account a generalized steering vector based on a CD source model (CD-MUSIC) is first studied. We establish closed-form expressions of the DOA estimation bias and mean square error due to both the model error and the effects of a finite number of snapshots. The aim of this paper is also to determine when the point source assumption is acceptable for standard MUSIC. The analytical results are validated by numerical simulations and discussed in different configurations.     

基于稀疏互质电磁矢量阵列的MUSIC算法

为增加传统MUSIC算法可分辨的信号源数,该文构造了一对具有互质关系的2维稀疏电磁矢量阵列,并基于此阵列提出了一种基于3维平滑的MUSIC算法。该算法利用两个阵列间的互质关系形成具有更多自由度的互质差合成阵列,并基于3维(2维空域加极化域)平滑算法恢复其自相关矩阵的秩,达到应用于传统MUSIC算法的目的。该算法的最大优势是仅使用二阶统计量即可系统地增加了原阵列的自由度。计算机仿真结果表明所提算法能估计多于物理阵元数的信号且分辨率高。     

Passive Localization of Mixed Near-Field and Far-Field Sources Using Two-stage MUSIC Algorithm

Passive source localization is one of the issues in array signal processing fields. In some practical applications, the signals received by an array are the mixture of near-field and far-field sources, such as speaker localization using microphone arrays and guidance (homing) systems. To localize mixed near-field and far-field sources, this paper develops a two-stage MUSIC algorithm using cumulant. The key points of this paper are: (i) in the first stage, this paper derives one special cumulant matrix, in which the virtual ?steering vector? is the function of the common electric angle in both near-field and far-field signal models so that source direction-of-arrival (DOA) (near-field or far-field one) can be obtained from this electric angle using the conventional high-resolution MUSIC algorithm; (ii) in the second stage, this paper derives another particular cumulant matrix, in which the virtual ?steering matrix? has full column rank no matter whether the received signals are multiple near-field sources or multiple far-field ones or their mixture. What is more important, the virtual ?steering vector? can be separated into two parts, in which the first one is the function of the common electric angle in both signal models, whereas the second part is the function of the electric angle that exists only in near-field signal model. Furthermore, by substituting the common electric angle estimated in the first stage into one special Hermitian matrix formed from another MUSIC spectral function, the range of near-field sources can be obtained from the eigenvector of the Hermitian matrix. The resultant algorithm avoids two- dimensional search and pairing parameters; in addition, it avoids the estimation failure problem and alleviates aperture loss. Simulation results are presented to validate the performance of the proposed method.     

Source localization using vector sensor array in a multipath environment

Coherent signals from distinct directions is a natural characterization of the multipath propagation effect. This paper addresses the problem of coherent/fully correlated source localization using vector sensor arrays. The maximum likelihood (ML) and minimum-variance distortionless response (MVDR) estimators for source direction-of-arrival (DOA) and signal polarization parameters are derived. These estimators require no search over the polarization parameters. In addition, a novel method for "decorrelating" the incident signals is presented. This method is based on the polarization smoothing algorithm (PSA) and enables the use of eigenstructure-based techniques, which assume uncorrelated or partially correlated signals. The method is implemented as a preprocessing stage before applying eigenstructure-based techniques, such as MUSIC. Unlike other existing preprocessing techniques, such as spatial smoothing and forward-backward (FB) averaging, this method is not limited to any specific array geometry. The performance of the proposed PSA preprocessing combined with MUSIC is evaluated and compared to the Crame/spl acute/r-Rao Bound (CRB) and the ML and MVDR estimators. Simulation results show that the MVDR and PSA-MUSIC asymptotically achieve the CRB for a scenario with two coherent sources with and without an uncorrelated interference source. A sensitivity study of PSA-MUSIC to source polarization was also conducted via simulations.