有源欺骗干扰环境下的DOA估计

针对有源欺骗干扰环境下基于小样本的DOA估计问题,该文提出自适应极化滤波(APF)联合块稀疏贝叶斯学习(BSBL)算法的DOA估计方法。首先,通过APF抑制干扰能量,提高信干比。然后,建立有源欺骗干扰环境下的稀疏贝叶斯模型,基于相邻快拍相关性,利用BSBL算法进行DOA估计。仿真和实测数据处理结果表明,所提方法降低了干扰对BSBL算法的影响,且与APF联合子空间类算法或最大似然算法(ML)相比,具有更高的空间分辨率和DOA估计精度。     

基于块压缩感知的SAR层析成像方法

基于压缩感知(Compressive Sensing, CS)的SAR层析成像方法(SAR Tomography, TomoSAR),虽然实现了对目标的3维重构,但对于具有结构特性的目标其重构性能较差。针对这一问题,该文提出了采用块压缩感知(Block Compressive Sensing, BCS)算法,该方法首先在CS方法基础上将具有结构特性的目标信号重构问题转化为BCS问题,然后根据目标结构特性与雷达参数的关系确定块的大小,最后对目标进行块稀疏的l1/l2范数最优化求解。相比基于CS的SAR层析成像方法,该方法更好地利用了目标的稀疏特性和结构特性,其重构精度更高、性能更优。仿真数据和Radarsat-2星载SAR实测数据的试验结果验证了该方法的有效性。      

基于联合块稀疏模型的随机调频步进ISAR成像方法

在回波数据稀疏、低信噪比等不利条件下,利用随机调频步进信号进行ISAR成像时,成像性能将会严重下降。针对上述问题,该文在充分分析随机调频步进信号回波特性的基础上,提出利用目标距离向具有的联合块稀疏特征来获得高质量ISAR图像的新方法。首先,推导了在随机调频步进信号发射波形条件下目标回波信号的联合块稀疏成像模型并分析了该模型特征;其次,提出了联合块稀疏正交匹配追踪稀疏重构算法(JBOMP)实现对模型的求解。该算法利用ISAR回波信号具有的块稀疏以及联合稀疏等先验信息,因此在低量测值、低信噪比条件下的ISAR成像性能得到了增强。所提算法还可以实现对多维信号的联合处理,且具有较快的运算速度。理论分析与仿真实验均验证了所提方法的有效性。     

基于压缩感知的直扩通信多音干扰抑制

针对直扩通信多音干扰抑制算法应用受限于采样率较高的问题,在分别构建信号和干扰稀疏字典的基础上,利用正交匹配追踪算法,设计了一种压缩域直扩通信多音干扰抑制算法,并通过理论分析和计算机仿真验证了算法的有效性。仿真结果表明,在已知干扰稀疏度的条件下,该方法能够有效抑制多音干扰,干扰抑制效果不随干扰数量、干扰强度变化而变化,在压缩率为1/2、干信比为20 dB的条件下重构信号与加性高斯白噪声信道中传输信号解调性能相比只有约5 dB的信噪比损失。这将为在多音干扰条件下压缩采样后直扩信号的重构提供一种有效方法。     

基于块稀疏贝叶斯模型的ISAR成像方法

传统ISAR稀疏成像主要针对独立散射点散射系数的重构问题,然而实际情况下目标散射点之间并不是独立存在的,而是以区域或块的形式存在,在该情形下利用常用的稀疏重构算法并不能完全地刻画块状目标的真实结构,因此该文考虑采用块稀疏重构算法进行目标散射系数重建。基于块稀疏贝叶斯模型和变分推理的重构方法(VBGS),包含了稀疏贝叶斯学习(SBL)方法中参数学习的优点,其利用分层的先验分布来表征未知信号的稀疏块状信息,因而相对于现有的恢复算法能够更好地重建块稀疏信号。该方法基于变分贝叶斯推理原理,根据观测量能自动地估计信号未知参数,而无需人工参数设置。针对稀疏块状目标,该文结合压缩感知(CS)理论将VBGS方法用于ISAR成像,仿真实验成像结果表明该方法优于传统的成像结果,适合于具有块状结构的ISAR目标成像。     

针对块稀疏信道的估计算法

无线多径信道中存在着块稀疏结构。针对块稀疏信道中分块信息是否已知的不同场景,分别提出了两种基于块稀疏贝叶斯学习(BSBL)框架的OFDM系统信道估计算法。这两种算法根据边界最优(BO)方法估计信道分块的稀疏度参数,提升算法运算速率。为进一步提升信道估计性能,在基于BSBL框架算法仅利用导频信号估计信道的基础上,又提出了基于联合块稀疏贝叶斯学习(JBSBL)的信道估计新算法,该算法利用导频与数据子载波实现信道的联合估计。仿真结果表明,与传统的最小二乘算法比较,本文提出的算法均可获得很好的信道估计性能,且基于JBSBL的信道估计算法性能更佳。      

基于贝叶斯压缩感知的复数稀疏信号恢复方法

该文利用复数稀疏信号的时域相互关系提出一种新的稀疏贝叶斯算法(CTSBL)。该算法利用复数信号的实部与虚部分量具有相同的稀疏结构的特点,提升估计信号的稀疏程度。同时将多个测量信号间的内部结构信息引入到了信号恢复中,使原始的多测量稀疏信号恢复问题转变为单测量块稀疏信号恢复问题,使恢复性能得到了提升。理论分析和仿真结果证明,提出的CTSBL算法相较于目前的针对复数信号的多测量矢量贝叶斯压缩感知(CMTBCS)算法和块正交匹配追踪算法(BOMP)在估计精度上具有更好的性能。     

基于块稀疏度与自适应迭代的压缩感知方法

针对分块压缩感知算法在平滑块效应时损失了大量的细节纹理信息,从而影响图像的重构效果问题,提出了一种基于块稀疏信号的压缩感知重构算法。该算法先采用块稀疏度估计对信号的稀疏性做初步估计,通过对块稀疏度进行估算初始化阶段长,运用块矩阵与残差信号最匹配原则来选取支撑块,再运用自适应迭代计算实现对块稀疏信号的重构,较好地解决了浪费存储资源和计算量大的问题。实验结果表明,相比常用压缩感知方法,所提算法能明显减少运算时间,且能有效提高图像重构效果。     

非均匀块稀疏信号的压缩采样与盲重构算法

该文对非均匀块稀疏信号的压缩采样速率下限进行了分析,并对测量矩阵的约束等距常数衰减特性进行了理论证明.在此基础上,提出了一种块稀疏阶数和块分布未知情况下的非均匀块稀疏信号盲重构算法,按照逐次递减的块长度,对非均匀块稀疏信号进行多次均匀切割,利用正交匹配追踪算法逐次剔除均匀块中的零值位置,从而精确估计信号中非零块位置,实现信号的准确重构.理论分析了算法的性能,仿真实验进一步验证了算法的有效性和实用性.     

基于压缩感知的信号重构

压缩感知是针对稀疏或可压缩信号,在采样的同时即可对信号数据进行适当压缩的新理论,采用该理论,可以仅需少量信号的观测值来实现精确重构信号。文中概述了CS理论框架及关键技术问题,介绍了信号稀疏表示、观测矩阵和重构算法。最后仿真实现了基于压缩感知的信号重构,并对正交匹配追踪(OMP)重构算法性能作了分析。     

Adaptive interference suppression for DSSS communications based on compressive sensing

The existing interference suppression algorithms for direct sequence spread spectrum (DSSS) communications are confined to the high sampling rate. The compressive sensing is addressed to solve the problem in this paper. Firstly, the mathematical model of interference suppression in compressed domain is introduced; the DSSS signal and interference sparse dictionary is built. Secondly, according to the difficulty in obtaining the prior information of the interference signal sparse degree, the adaptive interference suppression algorithm is proposed by setting the reasonable control threshold. A comprehensive analysis and comparison of the algorithm are presented and discussed. The numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm. The results show that the algorithm could suppress the interference effectively; the interference suppression performance does not change with the interference intensity and interference quantity. This will provide an effective method for the reconstruction of the compressed DSSS signal under the scenario of interference. The results obtained here may also be applicable in alternative spread spectrum technologies, like code division multiple access system.     

Training Sequence Design for NBI Mitigation in Compressive Sensing UWB Systems

Ultra wideband (UWB) is a promising technology in delivering high data rate for short range wireless communication systems. Because of their large bandwidth, UWB signals may encounter some problems especially with high sampling rate requirements. Moreover, coherence existence with other narrowband systems is a major concern which needs to be addressed through proper mechanisms. The problem becomes so complex if multiple users exist. Since narrowband interference (NBI) signals have sparse representation in the discrete cosine transform (DCT) domain, they can be estimated and suppressed using Compressive Sensing (CS). CS also has the ability to reduce the high sampling rate requirements. For training based NBI mitigation with CS, three groups of pilot symbols are used to estimate the NBI signal subspace, the UWB signal subspace, and to provide information about the channel. In this paper, the distribution of pilot symbols among the three groups is investigated in the presence of strong NBI. The investigation is based on the bit error rate performance and throughput. The influence of each pilot symbols group is studied. The performance is also evaluated in the presence of multiuser interference in addition to the NBI. Simulation results show that the size of the third group of pilot symbols which is used to estimate the channel is the most dominant one.     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。      

Interference suppression for ultra dense network based on compressive sensing framework

Ultra-dense network (UDN) deployment of small cells introduces novel technical challenges, one of which is that the interference levels increase considerably with the network density. This paper proposes interference suppression scheme based on compressive sensing (CS) framework for UDN. Firstly, the measurement matrix is designed by exploiting the sparsity of millimeter wave channels. CS technique is employed to transform the high dimension sparse signal into low dimension signal. Then, the interference is canceled in the compressed domain. Finally, the stagewise weak orthogonal matching pursuit (SWOMP) algorithm is used to reconstruct the useful signal after interference suppression. The analysis and simulation results demonstrate the effectiveness of the algorithm. Simulation results demonstrate that the proposed interference suppression in compressive domain yields performance gains compared to other classical interference suppression schemes. The proposed algorithm can reduce the computational complexity of interference suppression algorithm.     

正交预处理在DSSS信号压缩感知中的应用

直接序列扩频信号因具有良好的隐蔽性和抗干扰性能被广泛应用,压缩感知能有效降低直扩信号的采样速率。当通过冗余字典稀疏分解直扩信号时,观测矩阵和稀疏基一般有强相关性,该文提出正交预处理(Orthogonal Pretreatment:OPT)方法对观测矩阵和稀疏基进行预处理,降低观测矩阵与稀疏基之间的相关性,从而提高信息恢复的精度与稳定性,仿真结果表明提出的方法有效。     

基于SWCoSaMP算法的稀疏信号重构

压缩感知(compressed sensing, CS)稀疏信号重构本质上是在稀疏约束条件下求解欠定方程组。针对压缩感知匹配追踪(compressed sampling matching pursuit, CoSaMP)算法直接从代理信号中选取非零元素个数两倍作为支撑集,但是不存在迭代量化标准,本文提出了分步压缩感知匹配追踪(stepwise compressed sampling matching pursuit, SWCoSaMP)算法。该算法从块矩阵的逆矩阵定义出发,采用迭代算法得到稀疏信号的支撑集,推出每次迭代支撑集所对应重构误差的L-2范数闭合表达式,从而重构稀疏信号。实验结果表明和原来CoSaMP算法相比,对于非零元素幅度服从均匀分布和高斯分布的稀疏信号,新算法具有更好的重构效果。      

基于RLS算法的直接序列扩频抗窄带干扰技术

在敌方有意的窄带强干扰下,扩频通信系统可以利用时域自适应滤波算法来抑制干扰。介绍了基于时域预测的自适应滤波RLS算法,仿真结果表明其对DSSS系统中的窄带干扰有较好地抑制效果。