基于子空间分解的信道阶数估计算法

提出一种新的基于子空间分解的信道阶数估计算法.首先基于子空间分解,将观测向量自相关矩阵的几何子空间按照某一正整数La分解为“信号”子空间和“噪声”子空间,由“噪声”子空间构建了一个特殊矩阵Gv,提出了Gv特征分析定理,该定理表明当且仅当La等于信道阶数时,Gv奇异并有唯一的零特征值,并进行了详细的理论和实验证明;然后根据该定理,不断修正“噪声”子空间的大小,判定Gv的奇异性,完成信道阶数的估计.仿真证明该算法不但在低信噪比条件下具有很好的估计性能,而且当信道具有较小的初始和结尾系数时,也能达到很好的效果.     

Subspace algorithms for error localization with quantized DFT codes

Recently, a class of real-number Bose-Chaudhuri-Hocquengem codes known as discrete Fourier transform (DFT) codes have been considered as joint source and channel codes for providing robustness to erasures and errors over wireless networks. We propose three subspace algorithms for error localization with quantized DFT codes. The algorithms are similar to the MUSIC, the minimum-norm, and the ESPRIT algorithms used in array signal processing for direction-of-arrival estimation. They provide different but related formulations of the error localizations by first partitioning a vector space into the channel error subspace and its orthogonal complement, the noise subspace. The locations of the errors are determined from either the error subspace eigenvectors or the noise subspace eigenvectors. We also present a brief performance analysis of the localization error in terms of the perturbation of the error subspace due to quantization. Simulation results show that their localization performances are similar, and they perform better than the coding-theoretic approach over a broad range of channel-error-to-quantization-noise ratios.     

Computationally Efficient ESPRIT Method for Direction Finding

In this paper,a low complexity ESPRIT algorithm based on power method and Orthogo- nal-triangular (QR) decomposition is presented for direction finding,which does not require a priori knowledge of source number and the predetermined threshold (separates the signal and noise ei- gen-values).Firstly,according to the estimation of noise subspace obtained by the power method,a novel source number detection method without eigen-decomposition is proposed based on QR de- composition.Furthermore,the eigenvectors of signal subspace can be determined according to Q matrix and then the directions of signals could be computed by the ESPRIT algorithm.To determine the source number and subspace,the computation complexity of the proposed algorithm is approximated as (2log_2 n 2.67)M~3,where n is the power of covariance matrix and M is the number of array ele- ments.Compared with the Single Vector Decomposition (SVD) based algorithm,it has a substantial computational saving with the approximation performance.The simulation results demonstrate its effectiveness and robustness.     

基于子空间的MIMO-OFDM盲信道估计

基于子空间的MIMO-OFDM盲信道估计算法传输效率较高,但是算法计算量较大且接收端需要较多的信号才能进行精确估计.本文将基于噪声子空间的跟踪算法应用到MIMO-OFDM系统进行信道估计,并且对接收信号进行重构,达到了降低算法复杂度的目的,并减少了对接收信号数量的需要.     

基于噪声子空间跟踪的OFDM盲信道估计

基于子空间分解的OFDM信道估计算法利用信号子空间和噪声子空间的正交性可以对信道参数进行盲估计,但是子空间分解的运算量大,使盲信道估计算法的实用化受到限制。本文在噪声子空间自适应跟踪的基础上进行OFDM盲信道估计,显著降低了运算量。仿真结果表明,在适当选择学习因子后可以实现对噪声子空间的快速跟踪,在噪声子空间跟踪的基础上得到的信道估计性能接近于子空间分解法。     

一种实用性强的MC-CDMA盲信道估计方法

基于子空间分解的MC-CDMA盲信道估计算法运算量大,实用性差.本文根据在MC-CDMA中信号子空间维数远小于噪声子空间维数的特点提出从信号子空间的角度进行MC-CDMA盲信道估计,并采用PASTd算法对信号子空间及其维数进行动态跟踪.本文还通过对子空间跟踪的结果进行正交化处理的方法,显著提高了盲信道估计的性能.本文的盲信道估计方法运算量低,实用性强,仿真结果表明其盲信道估计性能接近特征值分解法.     

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.     

Optimal constrained representation and filtering of signals

A random signal is observed in independent random noise. We are addressing the problem of finding the optimum signal estimate that is constrained to lie in a given linear subspace. The optimality is defined in the sense of weighted mean square error. In the second step, we find the optimum linear subspace of given dimensionality. It is shown to be the linear manifold spanned by the eigenvectors of the simultaneous diagonalization of the signal and noise covariance, that correspond to the largest eigenvalues. The result is valid for both discrete and continuous time. For large observation time and stationary signals, it is shown that the constrained optimal estimate is determined by the two spectral densities and a weighted Fourier Transform of the noise observations. The above result applies to both discrete time and continuous time signals.The Wiener filter emerges as a special case of the constrained filtering estimate, when the linear subspace is enlarged to coincide with the total measurement space.     

MIMO-OFDM系统中的子空间半盲信道估计

本文主要讨论了MIMO-OFDM系统中的一种半盲信道估计算法.该算法基于子空间分解技术,利用信号子空间和噪声子空间的正交性将两者分离.针对盲信道估计存在计算量大及收敛速度慢等缺点,本文充分利用少量的导频,给出了一种基于子空间跟踪技术的半盲MIMO-OFDM信道估计算法.仿真结果表明,该算法在保持盲信道估计算法性能的基础上,明显加快了收敛速度、降低了计算量.     

基于特征分解的SAR射频干扰抑制方法

合成孔径雷达容易受到射频干扰的影响,严重影响成像质量。本文针对合成孔径雷达面临的射频干扰问题,分析了射频干扰（RFI）的信号特性和信号模型,根据RFI信号功率高、频带窄的特点,提出了基于特征分解的干扰抑制方法。其基本思想是:通过对观测数据矩阵进行特征分解,根据特征值分布构造干扰子空间和信号子空间,利用二者的正交性抑制干扰信号。本文首先介绍了特征分解方法的基本原理,在此基础上,基于MDL准则判断主特征值的个数,提出了基于特征值分布的RFI检测方法,并进一步,利用特征向量构造相应的干扰子空间和信号子空间,给出了基于特征分解的RFI抑制方法。基于SAR实测数据的仿真实验表明该方法可以有效的抑制RFI,且尽可能的保留目标回波。      

Statistical performance analysis of the algebraic constant modulus algorithm

This paper presents a large sample analysis of the covariance of the beamformers computed by the analytical constant modulus algorithm (ACMA) method for blindly separating constant modulus sources. This can be used to predict the signal-to-interference plus noise ratio (SINR) performance of these beamformers, as well as their deviation from the (nonblind) Wiener receivers to which they asymptotically converge. The analysis is based on viewing ACMA as a subspace fitting optimization, where the subspace is spanned by the eigenvectors of a fourth-order covariance matrix. The theoretical performance is illustrated by numerical simulations and shows a good match.     

异步DS-CDMA系统盲空时信道估计及多用户检测

该文提出了适用于频率选择性瑞利衰落信道中的异步DS-CDMA系统盲空时信道估计及多用户检测算法。通过研究多径信号码空间和数据矢量空间,采用噪声子空间技术进行异步DS-CDMA系统盲空时信道参数估计,同时利用了多径传播和接收机同步失调的特性,以利于把盲线性滤波优化技术应用于稳健的干扰抑制。使用一种修改的ULV更新算法进行噪声子空间跟踪,该算法不需要相关矩阵的秩估计,直接估计噪声子空间,不进行信号子空间跟踪。并且研究了线性约束最小方差(LCMV)盲空时多用户检测及其基于Householder变换约束最小均方算法(HCLMS)的自适应实现。仿真结果验证了该文算法的有效性。     

Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices

A novel blind channel estimation algorithm, based on fourth-order cumulant matrices, is proposed and applied to linear Space–Time Block Coded (STBC) for Multiple Input Multiple Output systems. Contrary to subspace and Second-Order Statistics (SOS) methods, the presented approach estimates the channel matrix without any modification of the transmitter. It takes advantage of the statistical independence of the signals in front of the space–time encoding. In this paper, the presented algorithm estimates the channel matrix by minimizing a cost function based on the higher cumulant matrices after Zero-Forcing equalization to mitigate the computational complexity and improve the performance. We employ the proposed method to the STBC systems including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBC systems. Symbol error rate and Normalized Mean Square Error simulations of the proposed algorithm are shown for a different number of users, signal to noise ratios and different number of symbols per user in comparison with subspace and Second-Order Statistics (SOS) methods. The results show that the presented method performs well and outperforms other methods in estimating the channel matrix from the received data. Moreover, the proposed method presents high convergence speed in estimating the channel matrix.     

采用Lanczos算法快速估计噪声子空间

提出一种采用Lanczos算法估计噪声子空间的新方法。该方法采用传统的空间平滑技术解相干,然后由多级维纳滤波器的预滤波器的性质可知,多级维纳滤波器的冗余分解级的预滤波器可以构成一个噪声子空间。由此可以采用Lanczos算法快速估计到噪声子空间。由于不需要对协方差矩阵作特征值分解,而且所要求的冗余分解的级数较少,其运算量比基于特征值分解方法要小得多。此外,采用Lanczos算法计算降维矩阵和冗余矩阵只构成多级维纳滤波器的前向递推,从而使得算法的复杂度大大降低。最后,计算机仿真验证了该方法的有效性。     

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。     

基于子空间的空时分组码的解码

空时码解码算法往往假设信道状态信息(CSI)已经事先估计出,信道估计需要额外的运算量,而且信道估计误差使解码性能降低.本文提出一种基于子空间方法的空时分组码直接解码算法,利用空时分组码的结构,结合子空间算法,可以不需要信道信息,直接得到码符信息的闭式解.仿真实验结果表明,随着噪声子空间求解准确度的提高,直接解码算法性能逐渐接近使用确切信道信息的相关解码器.     

一种新的信号子空间宽带测向算法

宽带信号测向是信号处理研究的难点课题之一,经典的旋转信号子空间（RSS）变换算法需要知道和真实来波方向接近的方向初始值。不合适的方向初始值将会降低估计器的收敛能力,引起估计偏差,从而使算法的估计精度受预估计的影响很大。本文提出的新算法首先推导出不同频段不同方向角的方向矢量之间的关系,然后再利用信号子空间、噪声子空间和阵列流型之间的关系构造一个特殊矩阵,最后通过求条件数来判断来波方向。所以新算法不需要构造聚焦矩阵,不需预知初始值,从而提高了算法的稳健性,降低了运算量,且新算法同时利用了信号子空间和噪声子空间,减少了噪声子空间扰动对算法精度的影响。计算机仿真验证了新算法的有效性。     

MIMO-OFDM系统中信道盲估计与检测

针对MIMO-OFDM系统,提出了一种基于子空间的盲信道估计与检测方案,该算法将阵列信号处理的思想应用到MIMO-OFDM系统中,通过发送端信号的冗余编码,利用一种类ESPRIT算法进行盲信号检测和信道估计。仿真结果表明该算法的有效性及其信道盲估计方法的性能。     

基于联合像素特征对消动目标检测方法

提出了基于三通道联合像素的最小功率特征对消的方法来检测SAR图像中的地面运动目标。文中的方法不要求多天线间距、载机速度、脉冲重复频率满足特定的条件,它通过构造三通道联合像素矢量来产生协方差矩阵,然后对协方差矩阵进行特征分解,选取噪声的特征向量来构成噪声子空间,最后利用噪声子空间来抑制地面杂波,从而检测出地面慢速运动目标;同时还分析了针对实测数据中存在系统误差的情况下选取不同的特征向量分别构成的噪声子空间对于该方法的运动目标检测性能的影响。实测数据处理结果验证了该方法的有效性。     

有色噪声背景下自适应雷达波形优化设计

仅以提高雷达检测性能为目标所设计的发射波形通常具有较高距离旁瓣和峰均比,不利于微弱信号检测和实际工程应用。针对上述问题,提出目标信号子空间加权的自适应波形优化方法。给出利用环境中有色噪声的统计特性改善目标检测性能的系统模型,选取目标-有色噪声矩阵的大特征向量构成目标信号子空间,建立在该子空间上优化加权系数使发射波形满足自相关性能和峰均比约束的目标函数,并采用序列二次规划算法进行求解。该方法所优化发射波形能有效抑制有色噪声,同时获得较好的自相关特性和峰均比,改善雷达综合性能。仿真结果表明了该方法的有效性和灵活性。