基于时空解析恒模算法的CDMA智能 天线系统盲信号分离

针对恒模算法只能由迭代算法实现和解析恒模算法分辨用户数受限等问题,本文在解析恒模算法基础上,提出空时解析恒模算法(ST-ACMA),并将其应用于CDMA智能天线系统盲信号分离.通过将传统时域或空域的恒模算法扩展到空时域,将求解盲波束权矢量的问题等效成信号子空间盲信号恢复问题,通过多个矩阵的联合对角化处理,闭式得到所有用户信号.数值仿真结果表明,该方法有效地解决了解析恒模算法可分辨用户数受阵元数限制的问题,提高了盲信号分离的性能,有效地克服了多址干扰.     

最小Rayleigh熵恒模信号分离算法

提出了一种最小Rayleigh熵恒模信号分离算法(MRE-AOCMSS)。该算法通过对代价函数作适当的变换, 得到新的协方差矩阵,然后构造出协方差矩阵的Rayleigh熵,再通过最小化Rayleigh熵得到用以恢复恒模信号的最佳权矢量。该算法能够准确地分离出期望信号并抑制掉干扰,从而有效地克服了常规恒模算法(CMA)对同信道干扰(CCI)功率大于信号功率的情况敏感这一缺点。计算机仿真结果说明了该算法的有效性。     

强干扰下的盲波束形成算法

针对恒模波束形成算法在干扰信号功率较强的情况下不能收敛到期望信号的问题,提出了一种基于独立分量分析的盲波束形成算法。该算法应用独立分量分析的方法来分离阵列接收信号,通过期望信号来向约束分离向量使其收敛到期望信号,算法在强干扰信号情况下能够收敛到期望信号、对非恒模信号有效并且运算量适中。理论分析和仿真实验表明提出的算法在强干扰和幅相误差存在的情况下相比恒模算法性能优越。     

线性约束差分恒模自适应阵列性能分析

线性约束差分恒模算法(LCDCM)能有效克服恒模算法(CMA)中存在的干扰捕获问题,但其性能受迭代步长的影响。本文对所需信号功率、干扰信号功率及所需信号和干扰信号空域相关系数对迭代步长取值和算法收敛速度的影响进行了分析,给出了保证算法收敛的迭代步长的取值范围。最后利用计算机仿真对上述理论结果进行了验证。     

多级恒模在强干扰下弱信号测向中的应用

当两个信号功率相差较大时,常规的空间谱估计算法往往不能估计出弱信号的DOA。文中将多级恒模阵列系统应用于强信号存在下弱信号的测向中,并在恒模阵列的自适应对消器部分推导了基于递归最小二乘的迭代公式,最后给出了弱信号测向算法的具体步骤。仿真结果表明此方法是有效的。     

Alpha稳定分布噪声下级联恒模阵列及其稳定性分析

该文研究Alpha稳定分布噪声下级联韧性恒模阵列的稳定性。首先,提出一种新型的信号对消器,把本级阵列捕获的信号从接收数据中消除,并分析了该对消器达到的稳定状态。然后,以韧性的波束形成器和新型的信号对消器组成级联恒模阵列。分析了恒模信号和Alpha稳定分布噪声在级联恒模阵列间的传递情况。数值仿真验证了理论分析的结论,并对级联恒模阵列的多信号恢复进行了模拟。     

一种微弱直接序列扩频信号的盲自适应解扩算法

针对微弱直扩信号扩频码的盲估计和信息码的盲解扩问题,本文提出了一种能同时分离直扩信号扩频码和信息码的非线性盲自适应恒模算法,达到了对直扩信号盲处理。本文首先提出了直扩信号的盲分离问题,然后详细分析推导了盲自适应随机梯度恒模算法,最后将该盲自适应随机梯度恒模算法应用到了对微弱直扩信号的盲分离中,并从理论上阐明了可以用该算法来实现直扩信号的盲分离。所提出的算法完全不同于以往的基于矩阵分解（奇异值分解、特征分解等）的伪码盲估计方法,它的存储开销量和计算量都比较小,可以实现对较长伪码构造的直扩信号的处理,而且它的计算速度较快,在某种程度上解决了传统的基于矩阵分解的方法在直扩信号的实时处理及实现上的困难。理论分析和数值结果都表明了所提方法能较好地工作在较低的输入信噪比条件下。      

基于均匀线阵的盲分离算法

针对现有的最小二乘恒模盲分离算法虽然能实现源信号分离但分离信号间仍存在相关性的问题,在建立均匀线阵接收模型和分析现有算法的基础上,提出了一种将阵列结构和已有算法相结合的修正小二乘恒模盲分离算法,并进行了实验仿真。仿真结果表明,修正算法有效地降低了分离信号间的相关性,将分离结果用于信号测向,实现了对空间信号源的高分辨率测向。     

鲁棒约束恒模自适应波束形成算法

线性约束恒模算法能够有效克服恒模算法中存在的干扰捕获问题,但在信号方向向量存在偏差的情况下,其性能将会受到影响.针对上述问题,本文提出了鲁棒约束恒模自适应算法并对其性能进行了分析.该算法收敛速度快,抗扰动性强,对信号方向向量的偏差具有较强的鲁棒性,改善了系统的输出信干噪比.仿真实验表明,与线性约束恒模算法相比,鲁棒约束恒模算法具有很好的性能.     

一种盲自适应空时多用户检测技术

空时多用户检测技术是一种有效消除CDMA系统中多址干扰和码间干扰的方法。目前很多此类方法都需要知道一些系统参数,其自适应实现需要训练序列。提出了一种结合恒模算法(CMA)和解扩重扩算法(DRA)的盲自适应空时恒模解扩重扩算法(STCMDRA),他能够有效利用信号的恒模特性以及用户已知的扩频信号信息来较好地抑制多址干扰及码间干扰。仿真结果进一步验证了该方法的性能。     

Asymptotic properties of the algebraic constant modulus algorithm

The algebraic constant modulus algorithm (ACMA) is a noniterative blind source separation algorithm. It computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem. We analyze its asymptotic properties and show that (unlike CMA) it converges to the Wiener beamformer when the number of samples or the signal-to-noise ratio (SNR) goes to infinity. We also sketch its connection to the related JADE algorithm and derive a version of ACMA that converges to a zero-forcing beamformer. This gives improved performance in applications that use the estimated mixing matrix, such as in direction finding     

Adaptive blind source separation and equalization formultiple-input/multiple-output systems

In this paper, we investigate adaptive blind source separation and equalization for multiple-input/multiple-output (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMO-CMA). Our analysis reveals that the MIMO-CMA equalizer is able to recover one of the input signals, remove the intersymbol interference (ISI), and suppress the other input signals. Furthermore, for the MIMO finite impulse response (FIR) systems satisfying certain conditions, the MIMO-CMA FIR equalizers are able to perfectly recover one of the system inputs regardless of the initial settings. We then propose a novel algorithm for blind source separation and equalization for MIMO systems. Our theoretical analysis proves that the new blind algorithm is able to recover all system inputs simultaneously regardless of the initial settings. Finally, computer simulation examples are presented to confirm our analysis and illustrate the effectiveness of blind source separation and equalization for MIMO systems     

A novel signal separation algorithm based on compressed sensing for wideband spectrum sensing in cognitive radio networks

In cognitive radio networks, since cognitive terminals use a shared wideband frequency spectrum for data transmissions, they are susceptible to malicious denial‐of‐service attacks, where adversaries try to corrupt communication by actively transmitting interference signals. To address this issue, in this paper, we propose a novel signal separation algorithm based on compressed sensing, which can not only recover the entire spectrum but also separate mixed occupying signals. Specifically, the proposed algorithm is executed following three steps: (i) each cognitive terminal attempts to recover all signals over an entire wideband spectrum employing the compressed sensing technique; (ii) all cognitive terminals send their recovered signals to the fusion center where a wavelet edge detection method is adopted to locate the spectrum edges of these signals and then divide the entire spectrum into several sub‐bands; (iii) the fusion center separates its received signals on each spectrum sub‐band into different categories according to their features. Both analytical and simulation results indicate that this novel compressed‐sensing‐based algorithm can effectively separate wideband signals at a low cost and combat interference of the malicious terminals in cognitive radio networks as well. Copyright © 2013 John Wiley & Sons, Ltd.     

一种基于恒模算法的多用户盲波束形成新方法

提出了一种适用于多用户的盲波束形成新方法.该方法基于最小二乘恒模算法,当最小二乘恒模算法收敛于某一信号后,我们导出了算法的权向量与其它用户波达方向的关系,进而在不同用户的波达方向上形成初始增益,从而实现对所有用户的波束形成.文中给出了算法的并行和串行实现方案.     

Statistical performance analysis of the algebraic constant modulus algorithm

This paper presents a large sample analysis of the covariance of the beamformers computed by the analytical constant modulus algorithm (ACMA) method for blindly separating constant modulus sources. This can be used to predict the signal-to-interference plus noise ratio (SINR) performance of these beamformers, as well as their deviation from the (nonblind) Wiener receivers to which they asymptotically converge. The analysis is based on viewing ACMA as a subspace fitting optimization, where the subspace is spanned by the eigenvectors of a fourth-order covariance matrix. The theoretical performance is illustrated by numerical simulations and shows a good match.     

一种基于盲源分离的雷达信号分选方法

盲信号分离是近几年才发展起来的,用于解决从混合观测数据中分离源信号的一门新技术,已在众多领域中获得了广泛的应用。文中结合高阶累积量对高斯噪声的不敏感性,给出了一种基于盲源分离的雷达信号分选方法,可以对高斯噪声背景中混合的多个雷达信号实现分选。最后给出了该方法的计算机仿真,结果证明了该方法的有效性。     

基于DOA估计的RLS-CMA算法

恒模算法被广泛地应用到盲自适应波束形成中,除了传输信号波形具有恒定的包络外,恒模算法不需要先验知识。提出一种基于来波方向估计的递推最小二乘恒模算法,基于恒模阵列级联的结构,由递推最小二乘算法决定恒模的初始权向量,同时通过对权向量多项式求根获得下一级的初始权向量,再利用最小二乘恒模迭代几步获得准确的结果。系统可以分离多个同信道信源,在干扰信号较强时,仍有稳定的信干比输出,并对阵列幅相差不敏感。计算机仿真证明了算法的正确性和有效性。     

非理想信道多用户数字信号的盲分离

在一个信道上传送多个用户信号,可以大大提高信道的容量,本文讨论了非理想信道多和户数字信号的盲分离问题,利用天线阵,接收可以看作是由Ｎ个独立信号源激励的非线传输混合系统的输出;由于信道存在的码间干扰,混合矩阵的元素不是常数,而是一个线性子系统,针对这一情况,本文提出了一个盲分离器结构,首先将接收信号进行盲分离,然后利用基于ＡＲ模型的盲均衡器消除每一路输出信号的码间干扰,从而实现多用户信号物分离,语文     

The constant modulus array for cochannel signal copy and directionfinding

The constant modulus (CM) array is a blind adaptive beamformer capable of recovering a narrowband signal among several cochannel sources without using a pilot or training signal. It is a conventional weight-and-sum adaptive beamformer whose weights are updated by the constant modulus algorithm. An adaptive signal canceller follows the beamformer to remove the captured signal from the array input and to provide an estimate of its direction vector. Based on a Wiener model, we investigate the steady-state properties of the CM array and the signal canceller. For mutually uncorrelated sources and noise, it is shown that the signal canceller exactly removes the source captured by the array. Thus, identical stages of the CM array and signal canceller may be used in a multistage system to recover several cochannel sources. Computer simulations are presented to verify the analytical results and to illustrate the transient behavior of the system