An efficient sensor quantization algorithm for decentralized estimation fusion

In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.     

RIS辅助毫米波系统中基于压缩感知的信道估计

针对可重构智能超表面（RIS）辅助无线通信系统中使用传统信道估计方法导频开销过高的问题,提出了一种基于块稀疏的正交匹配追踪（OMP）信道估计方案。首先,根据毫米波（mmWave）信道模型推导出级联信道矩阵,并将其转换到虚拟角域（VAD）中以获得级联信道的稀疏表示;其次,利用级联信道特有的稀疏特性将信道估计问题转换成稀疏矩阵恢复问题,并使用压缩感知的重构算法进行稀疏矩阵的恢复;最后,通过分析特殊的行-块稀疏结构,对传统的OMP方案进行优化,从而进一步减少导频开销并提升估计性能。仿真结果表明,与传统的OMP方案相比,所提出的基于行-块稀疏结构的优化OMP方案的归一化均方误差（NMSE）减小了大约1 dB。可见,所提出的信道估计方案能够有效减少导频开销并获得更好的估计性能。     

基于稀疏表示的OFDM信号的DOA估计

针对正交频分复用（OFDM）, 宽带信号波达方向(DOA)估计问题, 提出一种基于宽带信号协方差矩阵稀疏表示的DOA估计方法。该方法是在协方差矩阵主对角线下对左下角三角形元素按各条对角线取平均值后形成一个新的向量, 然后将该向量写成冗余字典形式。在冗余字典下对信号进行稀疏性约束形成二阶锥约束优化问题, 再用工具箱SeDuMi来实现DOA估计。理论分析和仿真结果表明, 该方法在低信噪比和少快拍数下分辨率很高, 是一种有效的宽带信号DOA估计算法, 此方法优于基于高阶累积量算法和宽带聚焦算法的DOA估计方法。     

无循环前缀单载波频域均衡及信道估计算法

单载波频域均衡(Single Carrier Frequency Domain Equalization,SC-FDE)技术可以有效地消除符号间干扰(ISI),相对于OFDM,具有较低的峰均比(PAPR),因此成为宽带无线通信关键技术之一。传统的SC-FDE系统需要在发送帧前面加入循环前缀(Cyclic-Prefix,CP),且CP长度要大于信道最大多径时延,这就降低了频带利用率,使得发射机功率增加。在取消CP情况下,通过隐训练序列获得信道脉冲响应估计信息,之后利用虚拟补零技术,恢复发送信号和信道的循环卷积,并通过迭代处理消除干扰。仿真结果表明,在信噪比大于3 d B后,算法误码率便接近相同信噪比条件下的高斯白噪声信道,性能较为理想。     

一种新颖的宽带短波探测系统音频干扰抑制算法

短波信道音频干扰分布密集,导致宽带短波探测系统接收信噪比严重恶化.针对传统奇异值分解(Singular value decomposition,SVD)滤波方法损失了干扰频率处的有用功率,对信道参数的高精度提取造成了不利影响.本文深入分析了SVD滤波内部机理,推导得到SVD滤波的解析表达式,并由信号加噪声子空间得出有用功率的鲁棒估计,进而提出了一种新颖的基于功率补偿的音频干扰抑制算法.仿真与实测数据处理结果表明,该算法不仅实现了干扰抑制,而且有效减小了抑制产生的信号损伤,对于短波电离层信道参数的高精度提取具有特殊意义.     

基于MUSIC算法的宽带频谱感知

利用MUSIC算法进行宽带频谱感知时,主用户信号个数估计是关键问题。为此,提出一种动态门限搜索匹配的信号个数估计算法。利用信号子空间和噪声子空间的正交性动态调整门限,搜索与预设维度最匹配的信号个数作为最终的估计值。仿真结果表明,在低信噪比的情况下,该算法能准确估计信号个数,提高宽带频谱感知性能。     

毫米波无线通信系统混合波束成形综述

毫米波频段拥有大量未充分使用的频谱资源,能有效缓解低频段频谱拥塞,并且由于毫米波波长较短,能极大地减小大规模天线系统的物理尺寸,使得毫米波通信成为5 G无线通信系统潜在的关键技术之一。考虑到毫米波传播路径损耗严重,毫米波系统需采用波束成形技术改善传输质量。在毫米波大规模多输入多输出(Multiple-input multiple-out, MIMO)系统中,由于数字波束成形高功耗、高成本问题,混合数模波束成形成为重要的替代方案。本文首先阐述了毫米波混合波束成形的研究现状,而后给出了系统模型,最后介绍了信道估计、码本设计和低复杂度设计等混合波束成形的关键技术。     

基于后置带通滤波的中频信号A/D变换精度提高方法

用低位A/D变换器通过滤除量化噪声达到高位A/D变换器的性能,一直是信号处理研究人员关注的问题。针对通信中常见的中频带通信号,本文提出了一种采用后置数字带通滤波器降低信号量化噪声、改善A/D变换精度的方法,以一种常见的中频带通信号为例,分析了该方法的性能,并通过仿真进行验证。结果表明,对A/D变换后的信号进行后置数字带通滤波处理,量化噪声大幅度降低,A/D变换性能得以明显提高。本文还对量化噪声的传统统计模型进行了改进,放宽了传统统计模型中量化噪声为白噪声的假设条件。     

OFDM系统群稀疏信道估计与译码迭代算法

针对OFDM通信中的多径信道估计问题,本文提出了一种群稀疏信道估计和译码迭代算法。在信道估计中,利用了无线多径信道的群稀疏特性,采用群基追踪降噪算法（Group basis pursuit de-noising,Group-BPDN）算法,提高了信道估计的精度。在译码时,采用软输出的维特比算法,从译码输出中提取可信度大的结果,反馈到信道估计的输入部分,与已知的导频信号一起构成新的已知信号集。这样,通过反馈迭代处理,一方面增加了稀疏恢复时的已知信息,提高了恢复的精度;另一方面,新的已知信号集和已估计出的信道响应可以用来估计噪声功率,提高了噪声环境下稀疏恢复算法的精度。仿真实验证明本文算法提升了信道估计性能,降低了误码率。     

Robust 1-bit compressive sensing via variational Bayesian algorithm

In a compressive sensing (CS) framework, a sparse signal can be stably reconstructed at a reduced sampling rate. Quantization and noise corruption are inevitable in practical applications. Recent studies have shown that using only the sign information of measurements can achieve accurate signal reconstruction in a CS framework. We consider the problem of reconstructing a sparse signal from 1-bit quantized, Gaussian noise corrupted measurements. In this paper, we present a variational Bayesian inference based 1-bit compressive sensing algorithm, which essentially models the effect of quantization as well as the Gaussian noise. A variational message passing method is adopted to achieve the inference. Through numerical experiments, we demonstrate that our algorithm outperforms state-of-the-art 1-bit compressive sensing algorithms in the presence of Gaussian noise corruption.     

State estimation for linear discrete-time systems using quantized measurements

In this paper, we consider the problem of state estimation for linear discrete-time dynamic systems using quantized measurements. This problem arises when state estimation needs to be done using information transmitted over a digital communication channel. We investigate how to design the quantizer and the estimator jointly. We consider the use of a logarithmic quantizer, which is motivated by the fact that the resulting quantization error acts as a multiplicative noise, an important feature in many applications. Both static and dynamic quantization schemes are studied. The results in the paper allow us to understand the tradeoff between performance degradation due to quantization and quantization density (in the infinite-level quantization case) or number of quantization levels (in the finite-level quantization case).     

基于奇异值分解的宽带频谱感知改进算法

对认知无线电中宽带频谱感知的信号检测问题进行研究,在信道位置和带宽随机分布的条件下,提出一种基于Hankel矩阵奇异值分解的改进算法完成宽带频谱的频率点奇异性检测并获得各子频段的起始频率和带宽,有效实现盲宽带频谱感知,同时利用提出的三种性能评价参数对该算法与多尺度小波变换算法进行了性能对比。通过实际接收微波信号及仿真OFDM信号感知实验验证,该算法有效抑制了噪声不确定性,滤除了伪奇异点,提高了宽带频谱感知性能。     

Detection of sparse targets with structurally perturbed echo dictionaries

In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels.     

Variational Bayesian inference based robust multiple measurement sparse signal recovery

This work concentrates on not only probing into a novel Bayesian probabilistic model to formulate a general type of robust multiple measurement vectors sparse signal recovery problem with impulsive noise, but also developing an improved variational Bayesian method to recover the original joint row sparse signals. In the design of the model, two three-level hierarchical Bayesian estimation procedures are designed to characterize impulsive noise and joint row sparse source signals by means of Gaussian scale mixtures and multivariate generalized t distribution. Those hidden variables, included in signal and measurement models are estimated based on a variational Bayesian framework, in which multiple kinds of probability distributions are adopted to express their features. In the design of the algorithm, the proposed algorithm is a full Bayesian inference approach related to variational Bayesian estimation. It is robust to impulsive noise, since the posterior distribution estimation can be effectively approached through estimating unknown parameters. Extensive simulation results show that the proposed algorithm significantly outperforms the compared robust sparse signal recovery approaches under different kinds of impulsive noises.     

大规模MIMO加权正交匹配追踪信道估计算法

针对大规模多输入多输出系统基站天线数目众多,移动用户很难实时精确完成信道估计等问题。提出了一种加权的正交匹配追踪算法。该算法在每次迭代过程中,计算得到的估计信号值由当前残差信号估计值和迭代之前估计值两部分组合而成;分别对当前残差信号估计值和迭代之前估计值设置不同的权值,以提高信号在低信噪比情况下的估值精度;通过调整不同迭代次数权值大小,可以提升信号在不同信噪比情况下的计算精度。仿真结果表明,在不同的信噪比情况下,该算法都可以获得比标准正交匹配追踪算法更高的估计精度。     

低压电力基于PLC的信道估计算法仿真

研究优化低压电力信道问题,针对低压电力线信道噪声污染严重,由于信道环境恶劣,造成误码率高。为了解决上述问题,采用OFDM系统中对低压电力信道的信道估计算法。传统LS(最小二乘法估计)算法复杂度低但性能受到限制,受噪声影响大。在综合考虑性能和复杂度,提出了新的改进的方法,并针对FFT的时域插值算法的缺陷,采用改进的算法进行仿真。仿真结果表明,改进算法使误码率降低,且减小了噪声的影响,证明信道估计算法提高了信噪比和估计的精度。     

宽带雷达信号的低杂散采样系统研究

为了实现宽带雷达系统中雷达信 号的低失真采集与处理,研究并设计了具有幅相误差校正功能的宽带低杂散采样系统。该系统采用宽带模数转换（Analog to digital converter, ADC）器件和高性能可编程逻辑阵列（Field programmable gate array, FPGA）的实现方案,并从低抖动采样时钟、低噪声电源和防串扰等方面进行了低杂散最优方案研究。为了改善系统的带内传输特性,利用优化算 法设计了有限长冲激响（Finite impulse response,FIR）数字校准滤波器。最后对设计 的系统进行实验测试,结果表明系统瞬时带宽达到800 MHz以上,采样率1.8GS/s,量化位数8位、杂散电平-50 dBc,性能指标满足系统在宽带雷达信号获取、宽带雷达目标成像和宽带雷达目标回波重构等领域的应用。     

光子学压缩感知技术研究进展

压缩感知是近来发展的一种新型的信号获取方法。根据压缩感知理论,频域稀疏信号可以以远低于奈奎斯特定律所规定的采样率进行采样和高精度的恢复。压缩感知在宽带信号获取中的应用将有利于降低对模数转换器的要求。最近,利用光子学技术实现基于压缩感知的稀疏信号获取引起了相关领域的广泛兴趣。光子学技术及其相关器件的应用可以大大提高系统的带宽,使得光子学压缩感知成为超宽带稀疏信号获取的一种很有前景的方法。本文综述了光子学压缩感知技术在稀疏信号获取中应用的研究进展,并给出一些研究成果。     

一种新型的数据采集系统性能量化方法的研究

考虑到数据采集系统的精度对应于系统对信号的不失真传输及系统本身的时间稳定性问题,提出了一种新型的数据采集系统性能量化方法。该方法将数据采集系统看作为一个开环的线性时不变系统,把采集系统本身看作滤波器,其输入为需要采集的信号,输出为采集到的信号,然后利用LMS算法的基本原理反推通道模型参数,最后利用求得的模型参数计算数据采集系统对正弦信号的传输特性,实现了对数据采集系统的性能考核。对系统模型和量化方法的数学仿真结果表明,所采取的量化方法和系统模型精确地反映了数据采集系统的正弦信号的响应,可以作为实际数据采集系统的精度考核手段。     

脉冲噪声环境下宽带循环平稳信号DOA估计算法

针对传统二阶循环相关算法在脉冲噪声环境中的显著退化问题,本文以α稳定分布为噪声模型,提出基于分数低阶循环相关的波达方向(Direction of arrival,DOA)估计算法。利用分数低阶循环相关的相移特性,将宽带循环平稳信号的DOA估计问题转化为"中心频率"为ε的窄带问题,解决了宽带情况下DOA估计困难的问题。计算机仿真结果进一步验证了此算法的有效性,且性能优于传统SC-SSF(Spectral correlation signal subspacefitting)算法。