非均匀阵列上相干信号的空间谱估计

本文提出了两种处理非均匀或任意形状阵列上相干信号空间谱估计的方法阵列数据变换法和不变子空间旋转法。前一种方法对阵列数据进行预处理使之可用已有的降维技术处理。后一种方法采取不变子空间旋转运算的途径获得多个线性独立矢量以构建信号子空间。不同于传统的降维方法,不变子空间旋转法不受阵列形状的限制,也不会损失阵列的有效孔径。计算机仿真的结果证实了本文方法的有效性。     

DOA estimation with double L-shaped array based on Hadamard product and joint diagonalization in the presence of sensor gain-phase errors

A method with double L-shaped array for direction-of-arrival (DOA) estimation in the presence of sensor gain-phase errors is presented. The reason for choosing double L-shaped array is that the shared elements between sub-arrays are the most and rotation invariant property can be applied for this array. The proposed method is introduced as follows. (1) If the number of signal is one, first the gain errors are estimated and removed with the diagonal of the covariance matrix of the array output. Then the array is rotated by an unknown angle and DOA can be estimated with the relationship between signal subspace and steering vector of signal. (2) If signals are more than one, the method for eliminating gain errors is the same with the previous case, and then the phase errors are removed by the Hadamard product of the (cross) covariance matrix and its conjugate. After the errors are eliminated, the DOAs can be estimated by rotation invariant property and orthogonal joint diagonalization for the Hadamard product. This method requires neither calibrated sources nor multidimensional parameter search, and its performance is independent of the phase errors. Simulation results demonstrate the effectiveness of the proposed method.

     

Two-dimensional equirotational stack subspace fitting with anapplication to uniform rectangular arrays and ESPRIT

A two-dimensional (2-D) multiple invariance technique for computing signal subspaces for uniform rectangular arrays (URAs) of size M×N sensors is introduced. The method is based on a multiple maximum overlap configuration of the sensors in the array with m×n subarrays of (M-m+1)×(N-n+1) sensors each. We exploit the fact that the stacked subspace of the subarray sensor output signals admits a two-level equirotational stack parametrization. We introduce a TLS-type algorithm for estimating the parameters of this equirotational stack subspace model. Based on this method of equirotational stack subspace fitting, the overall array signal subspace can be estimated with a much higher accuracy than with conventional unstructured SVD and TLS techniques. Detailed experiments validate the theoretical results. We propose a variant of 2-D ESPRIT based on equirotational stack subspace fitting. This 2-D equirotational stack ESPRIT (2-D ES-ESPRIT) algorithm clearly outperforms conventional unstructured variants of 2-D ESPRIT. A detailed comparison with 2-D unitary ESPRIT is presented     

Min-norm interpretations and consistency of MUSIC, MODE and ML

The multiple signal characterization (MUSIC) approach, its generalization to correlated signals known as the method of direction estimation (MODE), and the deterministic maximum likelihood (ML) approach for bearing estimation in array processing are shown to be signal subspace fitting approaches in a minimum norm sense. MODE, for example, is shown to be an approach in which the array manifold is linearly estimated from principal empirical eigenvectors in a minimum weighted Frobenius norm sense. Using the min-norm interpretations, a unified proof for strong consistency of the three approaches is provided for stationary and ergodic signals     

基于频域模型的宽带信号子空间谱估计方法

宽带阵列信号是频率的函数,因此其阵列流型及协方差矩阵都随频率变化.本文基于宽带信号的频域模型,通过分析宽带阵列信号协方差矩阵的特征分解结构,证明了宽带阵列信号噪声子空间不随频率变化的特性,并根据这一特性,提出了基于频域模型的宽带子空间谱估计(SSEFD)方法.应用K.Buckley的BASS-ALE方法解决了该方法用于均匀线阵时存在的频率-方位模糊问题.计算机仿真结果验证了SSEFD方法的有效性,与H.Wang的CSS方法的统计性比较表明,新方法具有更高的估计精度.     

基于特征空间逼近的传声器阵列语音信号增强

对现有的基于自动波束形成的传声器阵列语音信号增强算法提出了改进,将各传声器采集到的信号利用ABF(自适应波束形成)进行延时补偿并求和,消除信号中弱相干和不相干噪声;再利用特征空间逼近的方法进一步去除残留的噪声。将一种定阶方法应用到基于特征空间分解的语音信号增强中,利用其“最大稳定”原理,使得有效信号模型的阶数尽可能不受原始信号信噪比的影响,消除了传统定阶过程中的随意性和不稳定性。仿真结果表明:把自适应波束形成技术和特征空间逼近的方法结合起来,能够取得良好的去噪效果。     

Main flattening directions and Quadtree decomposition for multi-way Wiener filtering

Previous studies have shown that multi-way Wiener filtering improves the restoration of tensors impaired by an additive white Gaussian noise. Multi-way Wiener filtering is based on the distinction between noise and signal subspaces. In this paper, we show that the lower is the signal subspace dimension, the better is the restored tensor. To reduce the signal subspace dimension, we propose a method based on array processing technique to estimate main orientations in a flattened tensor. The rotation of a tensor of its main orientation values permits to concentrate the information along either rows or columns of the flattened tensor. We show that multi-way Wiener filtering performed on the rotated noisy tensor enables an improved recovery of signal tensor. Moreover, we propose in this paper a quadtree decomposition to avoid a blurry effect in the recovered tensor by multi-way Wiener filtering. We show that this proposed block processing reduces the whole blur and restores local characteristics of the signal tensor. Thus, we show that multi-way Wiener filtering is significantly improved thanks to rotations of the estimated main orientations of tensors and a block processing approach.     

Subspace Smoothing for Direction of Arrival Estimation in DS-CDMA Systems

In this paper a subspace processing method is introduced that can be used for direction of arrival estimation of coherent signals in an asynchronous DS-CDMA system. Conventional methods of direction of arrival estimation are not directly applicable to the case of multiple coherent signals that impinge on an antenna array from different directions. Some preprocessing is essential prior to estimation in this case. The proposed method exploits the spreading code and the path delays of the desired user to eliminate the contribution of undesired paths in the signal subspace. To this end, the signal subspace is mapped to a new subspace which contains the spatial-temporal signature of the desired signal. Once the desired subspace is created, conventional methods such as MUSIC and ESPRIT can be employed to estimate the desired directions of arrival. It is proved that the obtained direction of arrival estimator, based on the proposed method, is consistent. Also, the estimation performance is evaluated by comparing the proposed method with conventional estimation methods.     

Eigenspace-based adaptive array beamforming with robustcapabilities

This paper deals with adaptive array beamforming based on eigenspace-based (ESB) techniques with robust capabilities. It has been shown that ESB adaptive beamformers demonstrate the advantages of fast convergence speed and less sensitivity to steering angle error over conventional beamformers. In conjunction with a signal subspace construction method, we present an efficient technique to achieve the advantages of ESB adaptive beamforming with less computing cost and more robust capabilities over existing ESB techniques. Several computer simulation examples are provided for illustrating the effectiveness of the proposed technique     

一种新的基于 EEF 准则的空间声源个数估计算法

针对指数嵌入族（ Exponentially Embedded Families ,EEF）准则在快拍数小于阵元数情况下无法估计声源个数的问题,本文提出一种新的空间声源个数估计算法。首先通过球麦克风阵列采集空间声场高阶信息,建立球阵列信号模型,将声源个数估计扩展到三维空间。继而将观测信号空间分解为信号子空间和噪声子空间,利用最小均方差（ Minimum Mean-Squared Error ,MMSE）方法估计观测信号空间及噪声子空间的协方差矩阵,确保矩阵估计的一致性和准确性。在此基础上改进似然比函数,同时引入新的自由度计算,使得算法在快拍数小于阵元数的情况下能有效估计声源个数。仿真结果表明,在进行空间声源个数估计时,相对于EEF准则,新的算法不仅适用于快拍数小于阵元数情况,同时提高了估计准确率。     

广义复相关熵与相干分布式非圆信号DOA估计

针对相干分布式非圆信号参数估计算法在脉冲噪声环境下性能退化的问题,本文提出了广义复相关熵的概念,并给出了基于广义复相关熵的相干分布式非圆信号DOA（Direction of Arrival）估计方法。该算法首先由分布式信源模型获得入射信号的阵列输出信号,利用信号的非圆特性得到扩展阵列输出信号,再通过扩展阵列输出信号的广义复相关熵矩阵获取信号子空间,避开了传统二阶统计量算法在脉冲噪声下不适应的问题,最后由信号子空间旋转不变特性得到信号的中心波达方向角度。仿真实验结果表明,在Alpha稳定分布噪声条件下,与传统算法相比,本文所提算法具有更好的性能。      

Application of signal subspace approach to high-resolution narrowband array processing

The paper first briefly reviews some subspace techniques for high-resolution array processing. It is shown that existing high-resolution techniques like the MUSIC algorithm are based on visual inspection of the spatial spectrum. It is not a scientifically valid means of assessing resolution of a spectrum estimator. The paper then proposes a technique based on a combination of optimal processing and signal subspace extraction for high-resolution array processing. Numerical results show that the proposed technique not only achieves superresolution of the spectrum, but also provides power estimates of the arrivals.     

非均匀噪声背景下混合信号DOA估计算法

针对非均匀噪声背景下非相关信源与相干信源并存时波达方向(DOA)估计问题，提出了基于迭代最小二乘和空间差分平滑的混合信号DOA估计算法。首先，该算法利用迭代最小二乘方法得到噪声协方差矩阵估计，然后对数据协方差矩阵进行“去噪”处理，利用子空间旋转不变技术实现非相关信源DOA估计；其次，基于空间差分法消除非相关信号并构造新矩阵进行前后向空间平滑，利用求根MUSIC算法估计相干信源DOA。相比于传统算法，该算法能估计更多的信源数，在低信噪比情况下DOA估计性能更优越。仿真实验结果验证了该算法的有效性。      

Subspace-based error and erasure correction with DFT codes for wireless channels

This paper deals with the decoding of lowpass discrete Fourier transform (DFT) codes in the presence of both errors and erasures. We propose a subspace-based approach for the error localization that is similar to the subspace approaches followed in the array signal processing for direction-of-arrival (DOA) estimation. The basic idea is to divide a vector space into two orthogonal subspaces of which one is spanned by the error locator vectors. The locations of the errors are estimated from the spanning eigenvectors of the complement subspace. However, unlike the subspace approach in DOA estimation, which is similar to estimating the subspaces from the syndrome covariance matrix after a projection, in the proposed approach, the subspaces are estimated from the modified syndrome covariance matrix after a whitening transform. Simulation results with a Gauss-Markov source reveal that the proposed algorithm is more efficient than the coding theoretic approach on impulsive channels as well as the subspace approach with projection on lossy channels.     

基于信号子空间的多维参数联合估计

旋转不变的子空间中特征值的估计与关联是多维谱估计和阵列信号处理的重要问题.采用平行的交叉偶极子均匀线阵,利用各子阵列输出信号的相关矩阵构造多个旋转不变的子空间,并用其中一个子空间的信号子空间实现了频率、二维到达角和极化的自动配对.整个计算过程只需进行一次矩阵特征值的分解,就可完成多维参量估计与配对,且不存在参量兼并现象,理论分析与计算仿真结果证明了方法的有效性.     

A subspace rotation-based technique for estimating 2-D arrivalangles using nonlinear array configurations

This paper presents a subspace rotation based method to estimate 2-D direction-of-arrival (DOA) angles of narrowband signals radiated by far-field sources using planar and volume arrays. The new approach automatically pairs 2-D DOA angles by computing them directly from the normalized eigenvectors of a newly derived signal space matrix (SSM). This eliminates the constraint on subarray spacing and the need for a search procedure. Other advantages include adjustability in the accuracy of the DOA angles and maximum detectability of the master array     

互质阵列信号处理研究进展:波达方向估计与自适应波束成形

阵列信号处理是雷达领域各类应用的核心技术之一。近年来,互质阵列的提出打破了传统方法受限于奈奎斯特采样速率这一瓶颈,其稀疏布设的阵列结构和互质欠采样的信号处理方式大幅降低了系统所需的软硬件开销,为当前不断提升的实际应用需求提供了理论基础和技术前提。鉴于其在自由度、分辨率及计算复杂度等方面的性能优势,互质阵列信号处理的理论和技术研究受到了国内外学者的广泛关注。该文分别从波达方向估计和自适应波束成形这两个阵列信号处理领域的基本问题出发,介绍了互质阵列信号处理方向的研究进展。在互质阵列波达方向估计方面,该文总结了互质子阵分解方法和虚拟阵列信号处理方法等两类典型技术路线,并以此为基础介绍了压缩感知和无网格化技术在低复杂度和超分辨估计等方面的最新研究工作。在互质阵列波束成形方面,该文剖析了其与互质阵列波达方向估计问题的区别与联系,并介绍了面向互质阵列的高效鲁棒自适应波束成形设计方法。该文旨在通过对互质阵列信号处理研究前沿的分类归纳和总结,探讨各类方法的优势和未来的研究方向,为其在雷达等领域的产业需求和实际应用提供理论和技术参考。      

Signal subspace approach for narrowband noise reduction in speech

A signal subspace method is proposed for speech enhancement in the presence of narrowband noise. A fundamental assumption in subspace methods for noise reduction is that the noise covariance matrix is positive definite. However, this is not always the case, especially when the noise has narrowband characteristics. Based on the eigenvalue decomposition of the rank deficient noise covariance matrix, it is shown how to formulate the enhancement algorithm by decomposing the vector space of noisy signal into a signal-plus-noise subspace and a noise-free subspace. The proposed subspace partition is different from the conventional subspace approaches in that the noise reduction algorithm is implemented using the whitening approach exclusively in the signal-plus-noise subspace. The enhancement is performed by estimating the clean speech from the signal-plus-noise subspace and adding the components in the noise-free subspace. An explicit form of the estimator is presented, and examples are illustrated to validate the effectiveness of the proposed method.     

DOA estimation of LFM signals based on STFT and multiple invariance ESPRIT

In order to improve the angle measurement precision of LFM signals with a low computational complexity, a direction of arrival (DOA) estimation algorithm STFT-MI-ESPRIT is proposed in this paper. The algorithm is based on short time Fourier transform (STFT) and multiple invariance estimation of signal parameters via rotational invariance techniques (MI-ESPRIT). Firstly, the STFT of the array element’s output is calculated and the signals are transformed to the time-frequency domain. Then the spatial time-frequency distribution matrix can be obtained through selecting multiple single-source time-frequency points in the time-frequency plane and the signal subspace can also be obtained using Eigen decomposition. Finally, the multiple rotational invariant equation of the array based on STFT is obtained and the closed-form solution is obtained using the multi-least-squares (MLS) criterion. The simulation results show that the proposed algorithm can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and its computational complexity remains the same in general. This paper also proposes that the STFT-MI-ESPRIT algorithm can use partial rotational invariances of the array instead of all the rotational invariances, which can reduce the computational complexity on the basis of ensuring the estimation precision basically. The simulation results verify the effectiveness of the conclusion.     

一种基于信号子空间和听觉掩蔽效应的语音增强方法

采用子空间分解法对噪声语音进行信号增强，并用基于Johnston听觉掩蔽模型的感知滤波器对增强后的信号频谱进行平滑以抑制背景音乐噪声。几种噪声背景下对增强语音的客观测试表明，与传统的信号子空间分解法相比，基于信号子空间和听觉掩蔽效应的方法可有效地减少语音信号的失真度。