MEG covariance difference analysis: a method to extract targetsource activities by using task and control measurements

A method is proposed for extracting target dipole-source activities from two sets of evoked magnetoencephalographic (MEG) data, one measured using task stimuli and the other using control stimuli. The difference matrix between the two covariance matrices obtained from these two measurements is calculated, and a procedure similar to the MEG-multiple signal classification (MUSIC) algorithm is applied to this difference matrix to extract the target dipole-source configuration. This configuration corresponds to the source-configuration difference between the two measurements. Computer simulation verified the validity of the proposed method. The method was applied to actual evoked-field data obtained from simulated task-and-control experiments. In these measurements, a combination of auditory and somatosensory stimuli was used as the task stimulus and the somatosensory stimulus alone was used as the control stimulus. The proposed covariance difference analysis successfully extracted the target auditory source and eliminated the disturbance from the somatosensory sources     

基于协方差矩阵重构的阵元失效MIMO雷达DOA估计

在实际应用中由于恶劣环境或人为干扰等因素而导致多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达部分阵元失效,使得其接收数据缺失及其协方差矩阵秩亏,从而导致子空间类算法的波达方向(Direction of Arrival,DOA)估计性能恶化甚至完全失效。针对上述问题,提出了一种接收阵元失效下基于协方差矩阵重构的MIMO雷达DOA估计方法。该方法根据MIMO雷达协方差矩阵中以接收阵元数划分的子方块矩阵具有Toeplitz特性,利用正常工作接收阵元的协方差矩阵元素来恢复相应的缺失元素,从而重构出完整的数据协方差矩阵,提高阵元失效MIMO雷达的DOA估计性能。仿真结果验证了所提方法的有效性。     

基于IAA的协方差矩阵重构稳健波束形成方法

针对小样本背景下,存在相干信号、相位及阵元位置误差,传统波束形成方法性能不佳的问题,提出了基于迭代自适应法(IAA)的协方差矩阵重构稳健波束形成方法。该方法利用IAA估计出精确的功率谱,并进一步利用IAA估计的功率重构干扰协方差矩阵。重构过程中,将积分区域缩小到三维立体环域,减少无用信息的影响,提高了干扰协方差矩阵的重构精确度。最后通过波束形成抑制干扰信号。由于IAA不依赖于信号的非相干假设,解决了相干信号存在下的方位估计和功率估计。仿真表明,所提出的方法在相干信号、少快拍、相位及阵元位置误差同时存在的情况下,相对于其他波束形成方法,具有最优的信干噪比(SINR)输出,表明该方法具有优良的抑制干扰性能。     

一种基于协方差矩阵重构的波束形成算法

针对Capon波束形成在误差条件下敏感性问题,提出一种基于协方差矩阵重构的鲁棒波束形成算法。算法将信号集中出现的空域划分为干扰区域和信号区域,接着将两个区域划分为若干相互独立不重叠的部分,对干扰区域积分,构造出干扰协方差矩阵;再利用采样协方差矩阵特征分解后的最小特征值重构出噪声协方差矩阵;最后对期望信号导向矢量误差进行环不确定集建模,并在期望信号导向矢量环不确定集上进行Capon谱积分来估计期望信号协方差矩阵,根据其主特征矢量获取期望信号导向矢量。仿真表明,与传统鲁棒波束形成算法相比,此方法在不同快拍数以及输入信噪比条件下,性能更加优异且稳定,同时计算量较小。     

导向矢量和协方差矩阵联合迭代估计的稳健波束形成算法

针对自适应波束形成器在目标导向矢量存在约束偏差时性能急剧下降的问题,该文提出一种目标导向矢量和干扰噪声协方差矩阵联合迭代估计的稳健波束形成算法。该算法首先采用稀疏重构的方法得到目标导向矢量的初始值,并通过从采样协方差矩阵中剔除目标信号估计值完成干扰加噪声协方差矩阵的初始化;然后在建立导向矢量误差优化模型的基础上,采用凸优化方法对目标导向矢量和干扰加噪声协方差矩阵联合迭代求解。最后利用目标导向矢量和干扰加噪声协方差矩阵的稳态估计值获得自适应权矢量。仿真结果表明该算法提高了波束形成器在目标导向矢量约束偏差时的输出信干噪比。     

基于协方差矩阵双层重构的稳健自适应单脉冲测角

常规自适应单脉冲方法在主瓣干扰下会引起自适应波束形成性能恶化并导致其单脉冲比曲线严重失真,影响被动雷达的测角精度与跟踪性能。针对此问题,提出了一种适用于平面阵的基于协方差矩阵双层重构的稳健自适应单脉冲测角方法。首先,利用Capon功率谱通过稀疏重构法初步估计出干扰加噪声协方差矩阵,通过干扰导向矢量估计完成对协方差矩阵的优化校正以提高自适应波束形成性能;然后,基于线性约束最小方差(Linearly Constrained Minimum Variance, LCMV)准则对方位角和俯仰角进行联合线性约束以避免单脉冲比曲线在主瓣干扰下严重失真;最后,根据自适应单脉冲比值求出目标与波束指向之间的偏转角以实现目标测角。与常规方法相比,所提方法在干扰抑制能力与测角精度上都有显著提升。     

Estimating stationary dipoles from MEG/EEG data contaminated withspatially and temporally correlated background noise

The stationary dipole model for the inverse problem of magnetoencephalographic (MEG) and electroencephalographic (EEG) data is extended by including spatio-temporal correlations of the background noise. For that purpose, the spatio-temporal covariances are described as a Kronkecker product of a spatial and a temporal covariance matrix. The maximum likelihood method is used to estimate this Kronecker product from a series of trials of MEG/EEG data. A simulation study shows that the inclusion of the background noise generally improves the dipole estimate substantially. When the frequency of the source time functions, however, coincides with the frequency contents of the covariance function, the dipole estimate worsens when the temporal correlations are included. The inclusion of spatial correlations always improves the estimates     

基于嵌套阵列的稀疏表示稳健波束形成方法

针对波束形成中目标方位失配以及噪声加干扰的协方差矩阵非精确重构造成的波束形成方法性能下降的问题,提出一种基于嵌套阵列的稀疏表示稳健波束形成方法。在该方法中,计算嵌套阵的采样协方差矩阵,通过差合作阵处理得到一孔径扩展的虚拟均匀线列阵;基于稀疏表示的方法来估计目标以及干扰的准确方位信息;进一步利用得到的方位信息构造导向矢量,通过最小二乘方法计算干扰信号的精确功率值;最后重构干扰加噪声协方差矩阵,通过波束形成实现干扰抑制。数值仿真表明,所提方法有效提升了干扰加噪声协方差矩阵重构精确度,在不同信噪比和快拍数条件下,输出信噪比都能逼近最优信干噪比,验证了该算法的有效性。     

一种新的波束形成零陷展宽算法

针对自适应波束形成器在干扰位置出现扰动时的输出性能下降问题,该文提出一种新的零陷展宽算法。该算法基于投影变换与对角加载技术的结合,首先利用投影变换技术对阵列接收数据进行预处理,结合对角加载技术,以此构造出一个新的协方差矩阵替代原来的协方差矩阵,再利用自适应波束形成技术得到零陷展宽后的波束图。仿真结果表明,该方法能有效展宽波束零陷宽度,加深零陷深度,达到抑制位置出现扰动的强干扰信号目的。该算法易于求解,对参数的选取具有较强稳健性,在低快拍条件下,依然能有效地工作,增强了自适应波束形成器稳定性。     

A random dipole model for spontaneous brain activity

The statistical properties of the EEG and the MEG are described mathematically as the result of randomly distributed dipoles. These dipoles represent the interactions of cortical neurons. For certain dipole distributions, the first- and second-order moments of the electric and magnetic fields are derived analytically. If the dipoles are in a spherical volume conductor and have no preference for any direction, the variance of a differentially measured EEG-signal is only a function of the electrode distance. In this paper, the theoretically derived variance function will be compared with EEG- and MEG-measurements. It is shown that a dipole with a fixed position and a randomly fluctuating amplitude is an adequate model for the alpha-rhythm. An expression for the covariance between the magnetic field and a differentially measured EEG-signal is derived. This covariance is considered as a function of the magnetometer position, and is compared with the measurements of Chapman et al. [23]. The theory can be used to obtain a (spatial) covariance matrix of the background noise, which occurs in evoked potential measurements. Such a covariance matrix can be used to obtain a maximum likelihood estimator of the dipole parameters in evoked potential studies, to evaluate the merits of the so-called "Laplacian derivation," and for the interpolation of electromagnetic data.     

LTE上行通信链路中WiFi同频干扰的一种抑制方法

考虑多接收天线的第三代移动通信长期演进(LTE,long term evolution)上行通信链路,针对该链路中的Wi-Fi同频干扰,提出了一种新的多天线合并方法:干扰重建抑制合并(IRRC,interference reconstruction rejectioncombining)。首先估计接收信号的协方差矩阵,并将其作为干扰信号协方差矩阵的近似值;再应用干扰抑制合并(IRC,interference rejection combining)恢复期望信号;然后从接收信号中剔除期望信号,得到重建的干扰信号;最后,再次估计干扰信号的协方差矩阵,并进行干扰抑制合并。多径衰落信道中的仿真结果表明:考虑一发两收、正交相移键控(QPSK,quadrature phase shift keying)调制、0dB干信比的LTE上行信号,与传统的IRC方法相比,最小均方误差准则下应用IRRC方法约有2dB的发射功率改善。     

稀疏信号结构性噪声干扰下的感知矩阵优化

针对具有结构性噪声干扰的稀疏信号处理问题,该文提出一种基于贝叶斯理论的感知矩阵优化设计方法。结合具有加性干扰的稀疏信号模型,通过对感知矩阵进行能量约束,最小化信号的后验协方差矩阵的迹,实现感知矩阵的优化设计。仿真不同信号稀疏度和重构算法时,感知矩阵优化对信号重构误差和重构时间的影响;分析信号先验信息存在偏差时,感知矩阵优化对重构效果的影响。仿真结果表明,优化后的感知矩阵能够更好地获取稀疏信号中的重要信息,信号重构精度的均方误差减小约15～25 dB,重构时间减少约40%。     

四元数域干扰加噪声协方差矩阵重构鲁棒自适应波束形成

基于电磁矢量传感器阵列的四元数Capon波束形成器较传统的复数域Capon波束形成器有更好的性能。但是该方法在存在指向误差和极化失配的情况下性能急剧下降,甚至会出现信号相消现象。本文将协方差矩阵重构方法推广于四元数Capon波束形成中,通过利用Q-Capon的极化-角度谱估计得到干扰和噪声的功率来对干扰加噪声协方差矩阵进行重构,避免了对角加载方法中对对角加载因子的求解,而且能够有效克服指向误差与极化失配带来的性能下降。计算机仿真表明,该方法相较于其他四元数域的方法有着更好的性能。      

基于时域平滑约束的脑磁时序信号逆问题求解方法

由脑磁时序信号重建脑内时序神经信号时,除了要保证重建信号位置和强度的准确性,还要避免重建源信号在时域上瞬变.针对这一问题,提出了一种基于时域平滑约束的脑磁时序信号逆问题求解方法.该方法不同于传统最小范数估计算法（Minimum Norm Estimate,MNE）,通过引入时域平滑正则算子构造双参数混合正则化,根据广义交叉验证（Generalized Cross-Validation,GCV）原则选取双正则化参数后,根据单正则项的解在源信号中的权重将其进行线性组合估算出源信号.仿真数据实验表明,本文方法比传统MNE方法的总体均方误差小,且各时刻均方误差基本稳定在同一水平;同时本文方法重建的源信号与仿真源信号变化趋势基本一致.真实数据实验发现,本文方法重建结果的曲率变化率为0.0640,而传统MNE方法重建结果的曲率变化率为0.1646.实验结果证明本文方法能重建出空域准确且时域平滑的脑内神经信号.     

Experimental and theoretical comparison of some algorithms forbeamforming in single receiver adaptive arrays

Adaptive null steering in single receiver adaptive arrays is discussed. The single receiver structure allows only output power for a given set of weights to be measured. The problem, then, is to adaptively adjust the weights of the antenna array based on output power measurements only so as to reject interference signals while maintaining a fixed response in a given look direction. The authors determine the optimal beamformer weights in a single step, by estimating the covariance matrix of the array sensor outputs using a weight perturbation technique. Based on this covariance matrix estimate, three different approaches for finding the beamformer weights are studied. The first corresponds to a sample matrix inversion scheme, with the sample covariance matrix replaced by the one obtained from the perturbation estimation method, while in the second approach the weights are determined using an eigenvalue decomposition of the covariance matrix estimate. In the third approach the directions-of-arrival (DOAs) of the incoming wavefronts are first estimated, and then, in a second step, the beamformer weights are calculated from the DOA estimates. The advantage of the third approach is that this method is not affected by correlation between the different sources     

Average-intensity reconstruction and Wiener reconstruction ofbioelectric current distribution based on its estimated covariancematrix

Proposes two methods for reconstructing current distributions from biomagnetic measurements. Both of these methods are based on estimating the source-current covariance matrix from the measured-data covariance matrix. One method is the reconstruction of average current intensity distributions. This method first estimates the source-current covariance matrix and, using its diagonal terms, it reconstructs current intensity distributions averaged over a certain time. Although the method does not reconstruct the orientation of each current element at each time instant, it can retrieve information regarding the current time-averaged intensity at each voxel location using extremely low SNR data. The second method is Wiener reconstruction using the estimated source-current covariance matrix. Unlike the first method, this Wiener reconstruction can provide a current distribution with its orientation at each time instant. Computer simulation shows that the Wiener method is less affected by the choice of the regularization parameter, resulting in a method that is more effective than the conventional minimum-norm method when the SNR of the measurement is low     

基于原子范数的互质阵列协方差矩阵重构算法

针对互质阵列的虚拟阵列插值过程中协方差项的非均匀加权问题,将互质阵列协方差矩阵重构转换为低秩矩阵填充与原子范数重构,提出基于原子范数的互质阵列协方差矩阵重构算法。该算法先利用广义增广法得到非完备的互质阵列协方差矩阵,并利用截断的均值奇异值门限填充法得到虚拟阵列的协方差矩阵初值,然后对其进行原子范数最小化求解,实现稳健的正定Toeplitz协方差矩阵重构。该算法充分利用互质阵列协方差矩阵信息,有效提高互质阵列DOA估计算法的稳定性,降低计算复杂度。     

一种运动干扰稳健滤波方法

传统的阵列干扰抑制算法假设天线阵列与干扰之间相对静止,完成干扰抑制,当阵列与干扰之间存在相对运动时,存在协方差矩阵估计快拍数不足的问题,从而导致阵列滤波性能下降。针对上述运动干扰稳健滤波问题,提出了一种算法对现有零陷展宽技术抗运动干扰方法进行改进。一方面,将零陷展宽技术与导数约束方法相结合,用得到的新协方差矩阵求自适应权值;另一方面,为了提高自适应波束形成对噪声和误差的稳健性,引入了对角加载技术,加载量通过寻找协方差矩阵的特征值曲线拐点得到,有效避免了加载量过大导致的干扰信号被淹没以及加载量过小导致噪声分量引起性能损失的问题。该算法对零陷展宽系数选取依赖性较低,且对角加载量不需要设置固定的经验值,有利于稳健运动干扰滤波,适用于实际工程中先验信息不足条件下的自适应阵列抗运动干扰情况。     

基于Toeplitz矩阵重构算法的宽带相干信号方位估计

根据天线阵列接收到的数据,提出了一种基于Toeplitz矩阵重构的宽带相干源方位估计算法.该算法先由Toeplitz 阵列接收到的数据得到包含波达方向信息的协方差矩阵,再对该协方差矩阵进行聚焦处理,得到同一频率的阵列协方差矩阵,最后由高分辨子空间处理方法得到宽带相干信号的波达方向估计.仿真实验证明了该方法的有效性.