A two-stage algorithm for MIMO blind deconvolution of nonstationarycolored signals

A new two-stage algorithm is proposed for the deconvolution of multi-input multi-output (MIMO) systems with colored input signals. While many blind deconvolution algorithms in the literature utilize high order statistics of the output signal for white input signals, the additional information contained in colored input signals allows the design of second-order statistical algorithms. In fact, practical signal sources such as speech signals do have distinct, nonstationary, colored power spectral densities. We present a two-stage signal separation approach in which the first step utilizes a matrix pencil between output auto-correlation matrices at different delays, whereas the second stage adopts a subspace method to identify and deconvolve MIMO systems     

Generalized rectification of cross spectral matrices for arrays ofarbitrary geometry

In high-resolution methods applied to uniform linear arrays (ULA), the preprocessing that consists of forcing the estimated cross spectral matrix (CSM) to be Toeplitz by averaging its elements along its diagonals is known to increase the resolving power drastically. That is why it is always done in practice. However, this approach is limited to linear arrays because of the required Toeplitz structure for the CSM. This paper generalizes this technique to arrays of arbitrary geometry; the developed method is referred to as rectification. It proceeds by searching first for a vector subspace of Hermitian matrices that contains the manifold generated by the CSMs when the angle of arrival (AOA) varies. This preliminary step is performed only once for a given array geometry. Next, rectification of estimated CSMs is achieved by projecting them onto this subspace, resulting in denoising and increased resolving power of source localization methods at a very low computational cost. As a byproduct, the storage requirements for the CSMs are greatly reduced     

A novel wideband DOA estimator based on Khatri-Rao subspace approach

A novel DOA estimation method for uncorrelated wideband sources named focusing Khatri-Rao subspace method (FKR) is proposed based on coherent signal-subspace method (CSM) and Khatri-Rao (KR) subspace. Compared with the conventional CSM that simply averages the covariance matrices of different frequency bins after focusing, FKR transforms the covariance matrices into a higher dimensional matrix through KR product. This method has three major advantages: (1) it achieves a higher resolution than CSM, (2) the root mean square error of DOA estimation from FKR is smaller than that of CSM when the initial angles are inaccurate and (3) it performs well even when the number of sensors is reduced to about half of the sources. The performance of the FKR method is demonstrated and analyzed through the computer simulations.     

一种新的多目标角度功率联合跟踪算法

提出了一种新的多目标角度、功率联合跟踪算法。首先,利用高分辨处理估计出目标的个数、初始角度、初始功率及噪声功率。接着,通过相邻时刻估计得到的协方差矩阵的差推导出了一组关于角度差和功率差的线性方程组,重复解方程组可以得到不同时刻各个目标的角度以及功率。该算法可以减小阵元噪声带来的影响,在跟踪过程中,不需要进行子空间分解,参数自动配对,而且相邻时刻估计得到的角度和功率之间是自动关联的,省去了运算量较大的数据关联过程。进一步,给出了一种修正方法减小了算法中三个近似带来的误差。仿真结果表明,该算法的运算量低,跟踪精度高。     

A noise subspace projection approach to target signature detectionand extraction in an unknown background for hyperspectral images

A noise subspace projection (NSP) approach to extraction and subpixel detection of target signatures in an unknown background is presented. The proposed NSP approach is derived from a recently developed subspace orthogonal projection (OSP) method and can be shown to be approximated by an adaptive filter with the optimal weight given by the Wiener-Hopf equation. As a result, the operator resulting from the NSP approach can be used as an OSP operator for scene classification and subpixel detection, on one hand, and also implemented as an adaptive filter, on the other. These advantages make the NSP approach very attractive in practical applications. In particular, the NSP operator takes advantage of the noise subspace projection to prevent from inverting correlation matrices, as required by an adaptive filter     

快速子空间分解方法及其维数的快速估计

提出一种快速子空间分解方法.该方法只需要知道某一期望信号的训练信号就可以实现信号子空间和噪声子空间的快速估计.给出快速确定子空间维数的方法.子空间维数的估计和子空间的快速分解是同时进行的.本文方法只需要多级维纳滤波器的前向递推,不需要估计协方差矩阵和对其作特征值分解,所以运算量可以明显地降低,而估计的性能接近于常规方法.最后,仿真结果验证了方本文法的有效性.     

宽带源DOA快速估计方法

提出了一种新的聚焦矩阵的计算方法,该方法不需要进行角度预估计,也不需要进行任何矩阵特征值分解,对信号方向不敏感,运算量小。利用多级维纳滤波算法,求出聚焦后的空间协方差矩阵在Krylov子空间的一组正交基,在满足一定的条件下,该正交基张成的空间等价于信号子空间,从而可求得宽带信号的空间谱。整个过程避免了矩阵特征值分解,因而运算量降低。计算机仿真验证了方法的有效性。     

Adaptation dynamics of the spherical subspace tracker

L. Ljung's (1977) method for analyzing recursive stochastic algorithms is used to formulate a projection operator ordinary differential equation (ODE). The ODE describes the expected convergence dynamics of a noniterative spherical subspace tracker. The subspace ODE is a Riccati equation defined over the manifold of rank r projection matrices in C^nxn. A Lyapunov function is defined that is shown to have global maximum and minimum at the signal and noise subspaces, respectively. By taking a derivative of the Lyapunov function along any trajectory, it is shown that the dynamics force all trajectories to converge to the signal subspace. If the sign of the derivative is changed, all trajectories will converge to the noise subspace     

Single-trial subspace-based approach for VEP extraction

A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a prewhitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P(100), P(200), and P(300) of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P(100) is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.     

On the beamforming design for efficient interference alignment

An efficient interference alignment (IA) scheme is developed for k-user single-input single-output frequency selective fading interference channels. The main idea is to steer the transmit beamforming matrices such that at each receiver the subspace dimensions occupied by interference-free desired streams are asymptotically the same as those occupied by all interferences. Our proposed scheme achieves a higher multiplexing gain at any given number of channel realizations in comparison with the original IA scheme, which is known to achieve the optimal multiplexing gain asymptotically.     

蜂窝网络下行链路中基于干扰图的干扰对齐算法

针对多用户蜂窝网络的下行链路,提出了基于干扰图的干扰对齐算法。首先用干扰图表示蜂窝网络的小区间干扰（ICI）分布,每五个相邻小区划分为一簇。然后在簇内先根据对称信道和不对称信道,分别设计接收波束赋形矩阵将四个ICI对齐到两个干扰子空间,再设计发送波束赋形矩阵与干扰子空间正交。最后通过簇的平移实现蜂窝网络内所有活动小区的干扰对齐。该算法减少了基站和用户的天线数及计算复杂度。仿真结果表明所提算法消除了ICI、用户间干扰（IUI）及簇间干扰,有效提高了蜂窝网络容量。      

基于信号子空间的改进OFDM系统信道半盲估计

本文改进了一种基于信号子空间的OFDM系统半盲信道估计算法.该算法利用基于梯度变化的变遗忘因子递归最小二乘算法(GVFF-RLS)计算接收信号的自相关矩阵.通过同时对角化接收信号中的信息信号和噪声信号的全局协方差矩阵,推导出噪声信号子空间,无需对噪声信号的统计特性进行任何先验假定.本算法弥补了原始算法在慢衰落信道下收敛慢以及只限于加性白噪声的不足,实现了色噪声背景下高效半盲信道估计.仿真结果表明本文提出的算法具有良好的性能.     

一种数值稳健且低复杂度的信号子空间估计新方法

该文提出了一种数值稳健且低复杂度的信号子空间估计新方法。该方法通过多级维纳滤波器前向迭代构造观测数据协方差矩阵三对角化的转换矩阵,其列向量为信号子空间的一组正交基。与传统的相关相减结构结构相比,该文的多级维纳滤波器前向迭代通过Householder酉变换实现,显著增强了有限精度运算中信号子空间基向量的正交性,提高了数值稳健性。此外,基于Householder矩阵的酉性质和矩阵后向累积提出了一种转换矩阵的快速计算方法,降低了计算复杂度。计算机仿真结果验证了该方法的数值稳健性和计算效率。     

一种改进的降秩子空间盲多用户检测

提出了一种基于可变阈值的降秩子空间选择算法的盲降秩多用户检测技术。采用可变阈值的降秩子空间选择算法,能较快得到合适的降秩子空间,且计算结果具有可重用性。仿真结果表明,该算法能用较低的计算复杂度达到系统要求的检测性能。     

Novel criterion function for minor subspace tracking based on Rayleigh quotient

Aiming at the lack of information criterion functions of sub subspace,a novel criterion function was proposed by adding a penalty term to the Rayleigh quotient.Through analyzing the properties of all the stable points,it was proven that the criterion function exhibited the global maximum attained if and only if the weight matrices span the minor subspace.A minor subspace tracking algorithm was derived by gradient ascending method and its global convergence analysis was also accomplished.Numerical simulations and real application verifies the correctness of the criterion function and derived algorithm.     

On a Class of Orthonormal Algorithms for Principal and Minor Subspace Tracking

This paper elaborates on a new class of orthonormal power-based algorithms for fast estimation and tracking of the principal or minor subspace of a vector sequence. The proposed algorithms are closely related to the natural power method that has the fastest convergence rate among many power-based methods such as the Oja method, the projection approximation subspace tracking (PAST) method, and the novel information criterion (NIC) method. A common feature of the proposed algorithms is the exact orthonormality of the weight matrix at each iteration. The orthonormality is implemented in a most efficient way. Besides the property of orthonormality, the new algorithms offer, as compared to other power based algorithms, a better numerical stability and a linear computational complexity.     

正则化超声Lanczos反卷积的NCB成像分析

Lanczos方法是求解大尺度逆问题的一种有效方法,这种方法的特点是可以把大尺度问题转化为小尺度问题,而且可以把解严格限制在Krylov子空间,只是它存在的半收敛性问题需要进一步克服.为了确保算法的有效性、稳定性和精确性,Lanczos混合法(Lanczos-hybrid)试图通过正则参数的适当选取来解决这个问题.文章在Hansen提出的正则化参数选取的NCP方法基础上,设计了一种新的算法NCB,即利用Burg功率谱代替NCP中的经典周期图谱,较好地克服了Lanczos的半收敛性问题,降低了解对迭代次数的敏感性,得到了大尺度反卷积病态问题的稳定解;并以超声RF信号为例进行仿真,结果表明,NCB的成像效果比GCV要好.     

Matrix Description of Waveguide Discontinuities in the Presence of Evanescent Modes

The properties of the impedance and scattering matrix describing waveguide discontinuities are examined; both propagating and evanescent modes are considered. It is shown how different normalization conditions for the normal mode solutions in the guide affect the impedance matrix. A suitable choice of normalization always leads to a symmetric imaginary impedance matrix for a lossless structure. The scattering matrix is no longer symmetric or unitary.The simple relationship S=(Z- U)(Z+U)/sup -1/ is shown to hold only under special normalization conditions. Next the matrices describing a plane of lossless obstacles arranged in a periodic array are examined. A different type of normalization condition must be used here, since the normal modes are orthogonal in the conjugate sense (biorthogonal). Although the structure is reciprocal, none of the matrices is symmetric. A suitable normalization leads to a skew-hermitian impedance matrix and to a unitary submatrix of the scattering matrix corresponding to propagating modes.     

Time-frequency localised subspace tracking for narrowbandinterference suppression of DS/CDMA systems

A new narrowband interference suppression scheme based on time-frequency localised subspace tracking is presented. The received nonstationary signal is time-frequency-localised to extract stationary signal segments, on which reduced-order subspace tracking is applied to achieve interference suppression. Simulation reveals that this new scheme performs as well as the subspace decomposition scheme with a dramatically reduced computational complexity     

Sensor array processing based on subspace fitting

Algorithms for estimating unknown signal parameters from the measured output of a sensor array are considered in connection with the subspace fitting problem. The methods considered are the deterministic maximum likelihood method (ML), ESPRIT, and a recently proposed multidimensional signal subspace method. These methods are formulated in a subspace-fitting-based framework, which provides insight into their algebraic and asymptotic relations. It is shown that by introducing a specific weighting matrix, the multidimensional signal subspace method can achieve the same asymptotic properties as the ML method. The asymptotic distribution of the estimation error is derived for a general subspace weighting, and the weighting that provides minimum variance estimates is identified. The resulting optimal technique is termed the weighted subspace fitting (WSF) method. Numerical examples indicate that the asymptotic variance of the WSF estimates coincides with the Cramer-Rao bound. The performance improvement compared to the other techniques is found to be most prominent for highly correlated signals