一种空间非均匀噪声环境下DOA估计的改进方法

文中提出一种空间非均匀噪声环境下的信号源DOA估计的改进方法.该算法首先对阵列协方差矩阵进行平滑处理,在此基础上估计出空间非均匀噪声协方差矩阵,通过已估计的噪声协方差矩阵对平滑处理后的阵列数据协方差矩阵进行白化处理,克服了空间非均匀噪声对空间谱估计的影响,实现信号源DOA估计.相对于原方法,文中所提方法改善了DOA估计的性能,计算量小,并且能够估计强相关或相干信号源DOA.理论分析和仿真结果均表明所提方法的有效性.     

一种空间非均匀噪声环境下的DOA估计

针对空间非均匀噪声环境下的DOA(波达方向)估计问题,在中心对称分布的非均匀线性阵列上应用改进的恢复噪声协方差算法[6]得到一种新的DOA估计方法。由于非均匀阵列的阵列孔径更大,因此该方法可以估计空间相距更近的独立信号,另外该方法使用了平滑预处理技术,因此可以估计包含一对相干信号的信源,且所需的分辨信噪比门限更低。利用仿真实验验证了该方法的有效性。     

非均匀噪声条件下的互质阵列欠定DOA估计方法

针对基于互质阵列的欠定DOA估计方法在非均匀噪声条件下性能下降的问题,该文提出一种基于协方差矩阵重构和矩阵填充的鲁棒DOA估计方法。首先,将接收数据协方差矩阵分解,得到包含非均匀噪声项的对角阵;然后,选取对角线元素中的最小值,替换其余对角线元素,进而得到重构后的数据协方差矩阵;最后,对重构后的协方差矩阵进行扩展和矩阵填充,结合子空间方法进行DOA估计。理论分析和仿真结果表明,相对于现有方法,该文方法有效地抑制了非均匀噪声的影响,有更好的DOA估计性能。     

强干扰及非平稳噪声复合背景下相干信源的DOA估计方法

针对传统算法在强干扰及非平稳噪声复合背景下相干信源波达方向(DOA)估计精度低、鲁棒性不强等问题,文中提出了基于修正投影阻塞算法和Toeplitz矩阵重构的相干弱目标DOA估计。首先,通过接收信号的协方差矩阵进行特征值分解,构造与干扰导向矢量正交的修正投影矩阵作为干扰阻塞矩阵;然后,对接收阵列信号做预处理重新形成协方差矩阵,并对此矩阵进行Toeplitz重构解相干;最后,使用传播算子算法对重构后的协方差矩阵进行DOA估计,降低复杂度。仿真证明:该算法在复合背景下对相干信号的DOA估计比传统算法有着更高的精度和鲁棒性。     

空间非平稳噪声下的宽带DOA估计算法

投影子空间正交性测试(TOPS)法是利用子空间的正交性实现宽带信号DOA估计,而在空间非平稳噪声环境下子空间的正交性条件不再满足,尤其是在低信噪比或低快拍条件下子空间估计将出现较大误差,TOPS算法性能将急剧下降。针对该问题,提出了一种空间非平稳噪声下宽带DOA估计算法。该算法首先通过构造特殊对角矩阵将噪声从数据协方差矩阵中剔除,从而克服非平稳噪声对DOA估计的影响;然后利用平方TOPS法实现宽带信号DOA估计,消除了传统TOPS算法中的伪峰。该算法适用于空间非平稳噪声背景及低信噪比环境,提高了对角度相近目标的分辨性能;仿真实验表明了该算法的有效性。     

有色噪声下不相关和相干混合信号的DOA估计

针对有色噪声背景下的不相关和相干混合入射信号,本文提出了一种新的波达角度（Direction Of Arrival,DOA）估计方法 .首先对混合信号协方差矩阵进行分析和处理以消除其中的有色噪声部分.在此基础上,先利用多重信号分类（Multiple Signal Classification,MUSIC）方法或旋转不变信号参数估计（Estimation of Signal Parameters via Rotational Invariance Techniques,ESPRIT）法估计出不相关信号的DOA;然后利用改进的空间差分方法构造出一个新的只含有相干信号的协方差矩阵,且无秩亏损;最后利用MUSIC算法或ESPRIT算法从中估计出相干信号的DOA.和文献报道的方法相比,新方法具有更优的混合信号DOA估计性能,尤其对于相干信号.仿真结果验证了该算法的有效性.     

混合信号情况下信源数与DOA联合估计算法

针对非相关信源与相干信源共存情况,提出了一种基于矩阵重构的信源数与波达方向（direction of arrival,DOA）联合估计算法.该算法首先利用特征值的二阶统计量（second order statistic of eigenvalues,SORTE）法和子空间旋转不变技术（estimated signal parameter via rotational invariance techniques,ESPRIT）实现非相关信源数与DOA估计;然后基于空间差分法消除非相关信号并构造新矩阵,利用构造矩阵进行前向空间平滑,实现对相干信源解相干;最后利用SORTE法检测相干信源数,结合求根多重信号分类（multiple signal classification,MUSIC）算法估计相干信源DOA.与传统的差分平滑方法相比,该算法在可估计信源数与低信噪比情况下DOA估计性能等方面优于传统算法.数值仿真实验结果验证了该算法的有效性.     

基于阵列流型替换的宽带非相干DOA估计方法

提出了一种基于阵列流型替换的宽带非相干 DOA 估计新方法.比较了宽带非相干(ISSM)方法与本文方法在分辨两个相邻较近信号源时的性能.仿真表明,本文方法大大降低了信噪比门限,其它性能也优于 ISSM 方法.     

色噪声背景下相干信源DOA估计的空间差分平滑算法

文中提出了一种色噪声背景下相干信源波达方向(DOA)估计的新算法-空间差分平滑(SDS)算法.SDS算法利用均匀线阵协方差矩阵的Toeplitz分解特性,差分平滑运算,将非相干信源与相关(或相干)信源分开分辨,从而重复利用阵列接收数据,可分辨更多信源.SDS算法可对消空间色噪声,适用于更广泛的未知噪声背景及低信噪比环境.相比常规谱估计算法,SDS算法具有更强的信源过载能力及阵元节省能力,利用少数阵元进行迭代空间平滑运算,还可明显减小SDS算法的计算量.计算机仿真结果证明了SDS算法理论的正确性和有效性.     

基于噪声子空间特征值重构的DOA估计算法

该文针对非等功率信号波达方向(DOA)估计问题,提出一种基于噪声子空间特征值重构(Eigenvalue Reconstruction of Noise Subspace, ERNS)的超分辨算法。算法对接收信号自相关矩阵进行特征值分解,通过重构噪声空间特征值以及引入虚拟信源来构造新的接收信号自相关矩阵,对该矩阵进行特征值分解得到新的噪声空间特征值。当虚拟信源与实际信源入射方向相同时,新噪声空间特征值与重构后噪声空间特征值保持不变,利用这一特性来估计信源入射方向。该文给出算法的原理及实现步骤,并通过仿真进行原理验证与性能分析,仿真结果表明与其他子空间算法和MUSIC 算法相比,ERNS算法能够提高弱信号估计成功的概率。     

Maximum-likelihood direction-of-arrival estimation in the presenceof unknown nonuniform noise

We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai (1989, 1990) and Weiss and Friedlander (1993) to a more general noise model than the commonly used uniform one. In addition, these expressions extend the results obtained by Matveyev et al. (see Circuits, Syst., Signal Process., vol.18, p.479-87, 1999) to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case. To compare the estimation performance of the proposed ML technique with the results of our CRB analysis and with the performance of conventional “uniform” ML, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed     

ICA-based direction-of-arrival estimation of uncorrelated and coherent signals with uniform linear array

A novel approach for direction-of-arrival (DOA) estimation of uncorrelated and coherent signals with uniform linear array is proposed in this paper. First, the mixing matrix, which contains all azimuth information of signal sources, is estimated by independent component analysis. Afterward, several parameter equations are established upon the new mixing matrix. Finally, all DOAs of coherent and uncorrelated signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Moreover, the signal number resolved by our approach can exceed the number of array elements. Simulation results have demonstrated the efficiency of the proposed method.     

Two-dimensional direction of arrival estimation in the presence of uncorrelated and coherent signals

In this paper, a novel two-dimensional direction of arrival (2-D DOA) estimation method is proposed based on a new array configuration when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are estimated using the non-zero eigenvalues and corresponding eigenvectors of the DOA matrix (DOAM) combined with our proposed criterion. Meanwhile, we can form a new matrix without the information of uncorrelated signals. Then the coherent signals are resolved with the redefined DOAM that is constructed by the smoothed matrices of the new matrix. Simulation results demonstrate the effectiveness and efficiency of the proposed method. Other arrays that contain multiple identical central-symmetric subarrays (e.g. uniform rectangular arrays) can also be applied with our method.     

Maximum likelihood direction-of-arrival estimation in unknown noise fields using sparse sensor arrays

We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise.     

Gaussian Cramer-Rao bound for direction estimation of noncircular signals in unknown noise fields

This paper focuses on the stochastic Cramer-Rao bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models.     

Direction estimation in partially unknown noise fields

The problem of direction of arrival estimation in the presence of colored noise with unknown covariance is considered. The unknown noise covariance is assumed to obey a linear parametric model. Using this model, the maximum likelihood directions parameter estimate is derived, and a large sample approximation is formed. It is shown that a priori information on the source signal correlation structure is easily incorporated into this approximate ML (AML) estimator. Furthermore, a closed form expression of the Cramer-Rao bound on the direction parameter is provided. A perturbation analysis with respect to a small error in the assumed noise model is carried out, and an expression of the asymptotic bias due to the model mismatch is given. Computer simulations and an application of the proposed technique to a full-scale passive sonar experiment is provided to illustrate the results     

A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.     

一种波达方向、频率联合估计快速算法

首先提出了基于PM(propagator method)方法的波达方向(DOA)、频率联合估计快速算法，给出了PM算子的一个估计，由PM算子构造出一特殊的低维矩阵，其特征值给出频率的估计，进而由估计的频率和相应的特征矢量得到DOA的估计。该算法具有参数自动配对，计算量小的优点，易于在工程应用中实时处理。计算机仿真结果证实了算法的有效性。     

The optimum constant false alarm probability detector for relatively coherent multichannel signals in Gaussian noise of unknown power

In this paper, the method of "most powerful similar tests" is used to obtain the optimum (largest probability of detection) constant false alarm probability detector for multichannel signals received in the presence of additive Gaussian noise of unknown power. The signals are assumed to contain a common random phase angle, and hence are relatively coherent over the multiple channels. The noise is assumed to be correlated from channel to channel. The performance of the optimum detector is calculated. Finally, for illustrative purposes, the technique is applied to the detection of a signal in the presence of a jammer, and to the detection of a single channel signal in white and colored noise of unknown power.