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。     

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....     

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.     

抑制运动强干扰的稳健波束域目标方位估计方法

为了减小来自旁瓣区快速运动的强干扰对波束域高分辨方位估计方法的影响,提出一种稳健的波束域高分辨方位估计方法。该方法在形成多波束时,将稳健自适应波束形成与零陷扩宽技术相结合,有效地抑制了运动强干扰所造成的快拍失效和扫描方向误差引起的自适应波束图畸变,从而保证波束域方法能准确地估计目标方位。仿真结果验证了该方法的有效性。     

低快拍下基于RBF的CSB sin-FDA稳健波束形成

针对抑制与目标位置接近的平台外随队干扰的过程中,MVDR波束形成器在阵元数较大、快拍数不足时的主瓣畸变问题展开分析。以CSB sin-FDA接收阵列结构代替ULA-FDA接收阵列。快拍数不足时,将修正后的协方差矩阵导入模糊RBF神经网络计算最优权矢量,避免了矩阵求逆运算。与基于PA及FDA-BFF的MVDR波束形成器相比,在低快拍情况下,该方法在干扰位置处形成有效零陷的同时,主瓣指向能够保持在目标位置。且方向图仅在目标位置处形成单一主瓣峰值,这也为后续关于目标参数估计的一系列分析中的模糊消除奠定了重要基础。数值仿真验证了该方法的有效性。     

一种易于电路实现的鲁棒盲神经网络波束形成器

绝大多数通讯信号都具有周期平稳信号特性。利用信号的周期平稳特性可以进行真正的盲自适应波束形成,因而受到了广泛关注。鲁棒ＣＡＢ（Ｒ－ＣＡＢ）算法就是其中的一种,但其中涉及矩阵求逆,运算量很大。波束形成属于多维信号处理问题,阵列中阵元数的增加导致运算量成几何级数增加。文中提出了一种盲神经网络波束形成算法。由于神经网络具有网状计算结构的特点,故该方法不仅避免了矩阵求逆运算,同时更便于实时实现。该方法还利用对角加载技术,从而加快了收敛速度,保证了算法的鲁棒性。仿真实验表明其性能优越,易于电路实现     

基于Bayesian准则的鲁棒自适应波束形成算法

研究波束形成算法的前提是驾驶向量是确定的.在实际情况下,由于实际的阵列响应和期望信号的阵列响应存在一定的偏差,这时候就需要研究对于驾驶向量不确定情况下的波束形成问题.该文基于Bayesian准则,对一系列自适应MVDR波束形成器的权向量进行后验概率密度加权,得到Bayesian自适应波束形成器.仿真和实际水声阵列数据结果显示,该波束形成器在驾驶向量不确实的情况下,具有较好的鲁棒性.     

Multiuser Frequency Synchronization for Interleaved-OFDMA Uplink Systems

This paper investigates the frequency synchronization in the uplink of the Orthogonal frequency- division multiple access （OFDMA） system with interleaved subcarrier assignment. In such a system, one of the key problems is the multiuser frequency synchronization, which focuses on the multiple-parameter estimation of the Carrier frequency offsets （CFOs）. In this paper, we propose a two-stage frequency offset estimation algorithm. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. In addition, a novel CFO-compensation method is presented. Based on the inner signal structure of interleaved-OFDMA uplink, the new scheme uses adaptive beamformer to compensate the CFO of each user and isolate the signals of all users at the same time. Compared to the previously known methods, the proposed algorithm can provide accurate frequency synchronization and data detection without feeding CFO estimates back to active users for frequency adjustment.