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.     

近场MVDR聚焦波束形成被动定位方法

该文系统地研究了最小方差信号无畸变响应（MVDR）聚焦波束形成方法的定位性能,特别是对该方法定位性能受某个参数的影响情况进行了较详细的研究。首先分析了近场情况下线列阵接收信号的模型,然后分别推导了常规聚焦波束形成和MVDR聚焦波束形成的定位原理。仿真比较了这两种定位方法的定位性能,以及MVDR聚焦波束形成被动定位方法在不同的阵元间距和不同的目标距离情况下的定位性能。仿真结果表明,与常规聚焦波束形成相比,MVDR近场聚焦波束形成在提高分辨率的同时,减小了＂混叠＂影响,具有更为平滑的背景和更低的旁瓣级,同时认为,为了得到比较好的定位效果,目标应处于相对于＂近场＂的位置,从而为目标精确定位提供依据。     

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....     

基于最小均方无失真响应和支持向量机的被动声纳目标识别

为了有效地进行被动声纳识别,研究了一种运用最小均方无失真响应（Minimum Variance Distortionless Response,MVDR）谱系数作为特征参数,用多分类支持向量机作为分类器,进行被动声纳目标识别的方法。实验表明,在不同数目的训练样本情况下,基于最小均方无失真响应谱系数和多分类支持向量机的被动声纳目标识别方法使系统的性能显著提高,具有很好的识别效果和应用价值.其优于传统的神经网络作为分类器的识别方法,尤其是在训练样本较少情况下,识别率具有很大的提高。     

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。     

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.     

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.     

CW脉冲回波信号的最小方差无畸变响应自适应检测

建立了适用于解析时间序列信号检测的最小方差无畸变响应(Minimum Variance Distortionless Response,MVDR)自适应处理模型(Time Series MVDR,TSMVDR),并针对CW声纳脉冲回波信号,通过构造解析信号和分段近似处理给出了相应算法(Algorithms of TSMVDR on CW pulse signal,AWMVDR),并进行了数值仿真研究。研究结果表明:对于CW脉冲信号:(1)在较高采样率的条件下,AWMVDR可以实现CW脉冲信号稳定的自适应检测;(2)AWMVDR较常规线性相关检测具有更高的脉冲参数估计精度和时间分辨力;(3)AWMVDR的实现需要付出较高采样率和更大计算量的代价。