A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

最大稀疏稳健恒定束宽波束形成器设计

为了降低宽带阵列恒定束宽的实现复杂性,在分析宽带阵列稀疏性的基础上,构造了以阵元和抽头延迟线(TDL, tapped delay line)稀疏性的凸组合为目标函数,满足恒定束宽约束的波束形成器优化模型,降低了所需的阵元和TDL个数。引入重加权机制,通过序列凸优化,使稀疏性递增并收敛到最大值,证明了保证波束形成器稳健性的范数约束与最大TDL稀疏目标函数之间的等价性。仿真结果表明,可用较少的阵元及TDL个数获得相同的恒定束宽性能,具有工程实用价值。     

相位响应固定幅度响应约束的稳健波束形成方法

传统的幅度约束波束形成器是一个非凸问题,需将原始模型化为线性规划进行间接求解。该文针对均匀线阵提出一种相位响应固定幅度响应约束(PFMC)的稳健波束形成方法。利用权矢量逆序列对应的传递函数与阵列响应函数只差一个相位因子这一性质,将阵列响应的相位设置为固定的线性相位,仅对阵列响应的实数幅度进行约束,从而得到一个凸的代价函数,最优权矢量可以利用内点法求出。同时考虑到协方差矩阵误差,利用最坏(WC)情况性能最优原理提出PFMC-WC算法改善PFMC的性能。与传统幅度约束波束形成器相比,减少了约束个数并省掉了恢复权矢量过程,从而降低了计算量。此外,由于相位响应得到保证,该文算法相对于传统算法具有更好的性能。仿真实验验证了该文算法的有效性。     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.     

基于到达角误差约束的稳健宽带波束合成算法

针对阵列天线存在系统误差的情况,在Frost结构的基础上提出了一种改进的稳健宽带波束合成算法.该算法以信号到达角(DOA)误差为约束条件合成期望信号,并把滤波器优化设计转化为凸优化问题,使用内点迭代法有效解决,进一步使得期望的信号响应具有一定的波动性,从而使得更多的自由度应用在干扰和噪声的抑制上,达到优化加权制的目的....     

一种改进的大斜视小孔径SAR距离多普勒成像法

首先,分析了大斜视下小孔径SAR回波距离徙动的特点,其距离走动的影响远大于距离弯曲,可忽略距离弯曲和距离展开的高次项;然后,提出一种基于Keystone变换和秩亏Capon法改进的距离多普勒成像法.利用Keystone变换进行距离走动校正,可避开斜视角精确测试的难题.针对较少的方位向采样数据,利用非对角加载的秩亏Capon法进行方位脉压,可提高方位分辨率,获得高效的SAR成像.     

基于子空间的对角加载水平估计

Capon波束形成器通常利用对角加载方法来提高稳健性能。然而,对角加载方法的主要缺陷是不容易可靠地获得对角加载水平,从而影响加载效果。由子空间正交理论,噪声与信号子空间相垂直,因此当加载后的导向矢量与真实导向矢量重合时,加载后的导向矢量与噪声子空间垂直。基于这样的特性建立了一个代价函数。分析表明,这个代价函数为一凸问题,通过凸优化软件求解可以很容易地获得合适的加载水平,且与不确定集的参数值无关。仿真结果表明,利用该文获得的加载水平,Capon波束形成器能够有效地提高其稳健性能。     

空间非平稳噪声下圆阵的修正Capon算法

在空间非平稳噪声环境下,利用估计的噪声相关矩阵对圆阵接收数据相关矩阵进行预处理,可以消除非平稳噪声对方位估计的影响。再利用修正Capon算法,可以突破瑞利限的限制,且不需要知道信源数,从而实现目标的高分辨方位估计。仿真结果证实了该方法的有效性。     

波达方向估计算法的性能分析与仿真

波达方向估计是利用天线阵元对空间信源的来波方向进行估计.在二维平面,将来波方向和阵列法线的夹角定义为波达方向,由此定义阵列信号处理的基本数学模型.采用非参数化方法估计波达方向,包括Bartlett、Capon、MUSIC等方法.对其仿真表明,在估计精确度方面MUSIC法最好,Capon法次之,Bartlett法最差.     

Robust optimization of transmit/receive beamformer for MIMO downlink with imperfect CSI at base station

A robust scheme is proposed to jointly optimize transmit/receive beamformers for Multiple Input Multiple Output （MIMO） downlinks where the available Channel State Information （CSI） at Base Station （BS） （CSIBS） is imperfect. The criterion is to minimize the sum Mean Square Error （sum-MSE） over all users under a constraint on the total transmit power, which is a non-convex and non-linear problem. Observing from the first order optimization condition that the optimal transmit/receive beamformers are mutually dependent, the transmit/receive beamformers for each user are updated iteratively until the sum-MSE is minimized. Simulation results indicate that the proposed scheme can effectively mitigate the system performance loss induced by imperfect CSIBS.