智能天线自适应盲波束成形算法研究

智能天线中的自适应算法又称为数字波束成形算法,其中,盲自适应算法是不需要专门的训练信号或确定信号,节省了因接收训练序列和导频信号而占用的频谱资源,提高了频谱利用率.因此,盲自适应波束形成算法受到越来越多的关注.本文主要对现有的各种盲算法原理进行逐一介绍,然后对比了各种算法的优点.     

小波变换在智能天线波束形成技术中应用

智能天线是当前研究热点之一,首先介绍了波束形成的基本原理,按照盲算法和非盲算法将现有的波束形成算法进行分类并比较了算法的性能;其次,比较了基于小波变换和基于小波包的自适应波束形成算法,分析了基于小波变换的多分辨信号波达方向和基于小波神经网络的波束形成算法;最后,针对小波变换可在选择最优基方面和结合盲自适应算法进行联合分析方面进行了展望。     

基于峰度自然对数最大化的信号盲分拣算法和盲波束形成

该文基于峰度自然对数最大化准则,提出了一种自适应一元信号盲分拣算法,提出的算法可以用于一元信号盲分离和进行盲波束形成,与基于峰度值最大化准则的KMA算法相比,收敛速度快,有较强的稳健性,将非线性函数引入学习速率的调节,算法自动选取学习步长,避免了人工选取学习速率不当而导致算法发散。同时,提出了两种复数抽气算法,配合一元信号盲分拣算法可以依次分离多个信号源,仿真试验验证了算法的有效性。用提出的算法在四元线阵上盲分离两个水声信号,结果发现,一元信号盲分离实现的盲波束形成波束图与最优波束接近。     

基于自适应拟牛顿法的CDMA天线阵列波束形成

针对于CDMA系统自适应天线阵列,提出了一种新的盲自适应MSINR(Maximum Signal-to-Interference plus noise ratio)波束形成算法.首先,将MSINR准则转化为一种新的无约束损失函数,并且从理论上分析该损失函数的性质.然后,应用自适应拟牛顿方法得到在线迭代波束形成算法.该算法无需训练序,而是利用CDMA信号自身的结构特点,结合空间处理提高了系统性能.最后,给出了仿真结果,表明算法具有较快的收敛速度和良好的动态跟踪能力.     

基于子带阵列盲处理的空时干扰抑制

在卫星智能天线终端,传统空时自适应滤波处理中自适应算法需要信号信息而缺乏实时性,阵列处理算法复杂而抗干扰能力不足,针对此问题,提出了一种子带盲自适应阵列处理算法,用于直扩系统空时干扰抑制技术。子带阵列处理相对纯空域处理提高了阵列自由度,相对传统空时的抽头延迟线阵列自适应结构又大大降低了算法复杂度。提出的子带指数型变步长线性约束恒模算法的自适应阵列处理算法能在低算法复杂度下提供较高的收敛速度和收敛精度,不需要发送训练序列,可实现盲自适应波束形成,易于实现实时跟踪信号变化。仿真结果表明新的空时干扰抑制方案具有更好的抗干扰性能。     

基于多普勒信号的盲自适应波束形成技术

本文相控阵多普勒雷达盲自适应波束形的问题,利用信号的多普勒信息,提出了一种新的盲自适应波束形成方法。     

CDMA系统智能天线盲自适应波束形成

给出了基于最大化信号对干扰和噪声的功率比(MSINR)准则的盲自适应波束形成在线递推算法,旨在提高DS-CDMA系统中智能天线的性能.将MSINR准则转化为一种新的等价的无约束准则函数,通过最大化此准则函数来在线估计最优波束形成向量.算法直接利用了DS-CDMA系统解扩前和解扩后的观测信号,无须训练序列.通过在线迭代估计,降低了计算波束权值的复杂度.不同通信环境中的仿真结果表明,该算法具有较高的估计精度和很好的抗多址干扰能力.     

一种稳健的周期平稳信号的盲波束形成算法

因为不需信号个数和导向向量等任何参考信息,基于周期平稳信号的盲波束形成算法在信号处理领域得到非常广泛的应用,然而这类方法是建立在循环频率准确已知基础上的,在实际应用中往往不是如此,循环频率的估计误差将导致算法性能变差,为了改进这点不足,在传统的循环自适应波束形成(CAB)算法的基础上,提出了一种改进的 CAB 盲波束形成算法,经理论分析和计算机仿真,证明了该算法是有效的.     

一种基于频移滤波器的混合信号盲恢复算法

针对单天线接收的频谱混叠的混合信号盲恢复问题,在频移滤波器结构上,提出了一种基于相关函数误差准则的自适应频移滤波信号盲恢复算法.该算法利用滤波器输出信号和参考信号以 及混合信号与参考信号之间的相关函数误差来调整自适应滤波器输出权值.分析了该算法的稳态性能.仿真结果表明:在信噪比大于-5 dB的条件下,该算法对混合...     

存在循环频率误差时的盲自适应波束形成

绝大多数通信信号都具有循环平稳特性，利用信号的循环平稳特性可以进行真正的盲自适应波束形成，例如CAB类算法以及基于特征空间的盲波束形成算法^[3]。但当循环频率存在误差时，上述算法的性能将急剧下降。本文提出一种改进算法，在估计循环相关短阵时引入遗忘因子可以大大降低算法对循环频率误差的敏感性，计算机仿真验证了理论分析的正确性。     

Simple and practical cyclostationary beamforming algorithms

Inspired by the asymmetric principal component analysis (APCA) neural model and based on the signal cyclostationarity, the authors propose a simple gradient-descent beamforming (GDB) algorithm. The GDB algorithm is an adaptive blind beamforming algorithm and can be used to extract signals with cyclostationarity under a complex signal environment. Although the GDB algorithm suffers from slow convergence, it has a low computational complexity. Two more algorithms, called the boosted GDB and the beta GDB algorithms have been defined based on the GDB algorithm. All the algorithms have been simulated and compared. The boosted GDB and beta GDB algorithms are shown capable of providing fast convergence and satisfactory signal-to-interference-and-noise ratio (SINR) performance, and can be used for implementation in real-time systems.     

宽带恒定束宽盲波束形成算法

针对传统盲源分离算法对宽带阵列信号适用性较差的问题,提出一种基于时频分析的宽带恒定束宽盲波束形成算法。该算法首先将接收信号变换到时频域上并提取出单源点。然后,对单源点聚类并求解信号在不同频点上的导向矢量。最后,通过提出一种信号来向未知的空间响应变化约束方法,实现宽带恒定束宽盲波束形成。该算法避免了将宽带盲波束形成转换为卷积混合的盲源分离,因而不存在时域盲源分离算法中系统参数随滤波器阶数急剧增加的问题,也不存在频域算法中排序和幅度模糊的问题。仿真结果表明,算法能够较好地实现宽带信号的盲分离,且输出信干噪比高于时域、频域以及时频域盲源分离算法,实测数据的处理结果验证了该算法的实用性。      

利用PN码的盲自适应波束形成算法

本文基于卫星导航定位系统接收机的智能天线抗干扰应用背景。研究了利用PN码的盲自适应波束形成算法。首先介绍了LS-DRMTA算法，然后提出了DR-LMS类自适应波束形成算法。仿真结果表明，DR-LMS类算法具有和LS-DRMTA算法；DR-LMS类算法。     

基于SMI－CMA联合自适应算法的性能分析

恒模算法(CMA)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．CMA不需要先验知识。基于SMI—CMA算法的恒模(CM)阵列级联的结构，由SMI算法决定CMA的初始权向量．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SINR输出，具有较好的收敛速度。仿真结果也证明了SMI—CMA算法具有较强的稳健型和较快的收敛速度。     

Fast adaptive blind beamforming algorithm for antenna array in CDMA systems

In this paper, the maximum signal-to-interference-plus-noise ratio (MSINR) beamforming problem in antenna-array CDMA systems is considered. In this paper, a modified MSINR criterion presented in a previous paper is interpreted as an unconstrained scalar cost function. By applying recursive least squares (RLS) to minimize the cost function, a novel blind adaptive beamforming algorithm to estimate the beamforming vector, which optimally combines the desired signal contributions from different antenna elements while suppressing noise and interference, is derived. Neither the knowledge of the channel conditions (fading coefficients, signature sequences and timing of interferers, statistics of other noises, etc.) nor training sequence is required. Compared with previously published adaptive beamforming algorithms based on the stochastic-gradient method, it has faster convergence and better tracking capability in the time-varying environment. Simulation results in various signal environments are presented to show the performance of the proposed algorithm.     

Interference rejection using the time-dependent constant modulusalgorithm (CMA) and the hybrid CMA/spectral correlation discriminator

Two new blind adaptive filtering algorithms for interference rejection using time-dependent filtering structures are presented. The time-dependent structure allows the adaptive filter to outperform the conventional adaptive filter implemented with a time-independent structure for filtering of cyclostationary communication signals. At the same time, the blind adaption algorithms allow the filters to operate without the use of an external training signal. The first algorithm applies the CMA to an unconstrained time-dependent filtering structure. The second algorithm applies the CMA to a spectral correlation discriminator, which is constrained to select signals with unique spectral correlation characteristics. Using computer simulations, it is shown that the blind time-dependent filtering algorithms can provide mean-square errors (MSEs) and bit error rates (BERs) that are significantly lower than the MSEs and BERs provided using conventional time-independent adaptive filters. It is also shown that these processors can outperform the nonblind training-sequence directed time-independent adaptive filter     

Adaptive multistage beamforming using cyclic higher orderstatistics (CHOS)

A new blind beamforming algorithm for signals that exhibit higher order cyclostationarity is presented. Exploiting some previous theoretical developments, we show how cyclic cumulants of the received signals can be used to obtain the weights of the beamformer that perform blind extraction. The method is based on a spatial interpretation of a deconvolution procedure known as the super-exponential algorithm. The basic block processing algorithm is made fully adaptive using an adaptive URV scheme and applied to a typical mobile communications scenario where several cochannel interferers corrupt the signals of interest     

Fast adaptive super-exponential multistage beamforming for cellularbase-station transceivers with antenna arrays

A new blind adaptive beamforming algorithm is introduced. We show how cumulants of the received signals can be used to obtain the weights of the beamformer that perform blind extraction. The method is based on a spatial interpretation of a deconvolution procedure known as the super-exponential algorithm. The basic block processing algorithm is attractive because it can be transformed in an efficient adaptive algorithm which exhibits good tracking capability. To prove the effectiveness of the idea, we show results for a typical mobile communications scenario where several cochannel interferers corrupt the signals of interest     

Robust Adaptive Beamforming Under Quadratic Constraint with Recursive Method Implementation

When adaptive arrays are applied to practical problems, the performances of the conventional adaptive beamforming algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of performance degradation can occur because of data nonstationarity and small training sample size, when the signal steering vector is known exactly. In this paper, to account for mismatches, we propose robust adaptive beamforming algorithm for implementing a quadratic inequality constraint with recursive method updating, which is based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix. We show that the proposed algorithm belongs to the class of diagonal loading approaches, but diagonal loading terms can be precisely calculated based on the given level of uncertainties in the signal array response and data covariance matrix. The variable diagonal loading term is added at each recursive step, which leads to a simpler closed-form algorithm. Our proposed robust recursive algorithm improves the overall robustness against the signal steering vector mismatches and small training sample size, enhances the array system performance under random perturbations in sensor parameters and makes the mean output array SINR consistently close to the optimal one. Moreover, the proposed robust adaptive beamforming can be efficiently computed at a low complexity cost compared with the conventional adaptive beamforming algorithms. Computer simulation results demonstrate excellent performance of our proposed algorithm as compared with the existing adaptive beamforming algorithms.     

高信噪比低信干比环境下盲自适应波束形成

本文针对通信双方是合作方这一特点，利用信号的周期平稳性，提出了一种能够适应于高信噪比、低信干比环境的盲自适应波束形成算法，采用了特征向量识别的方法，较好地解决了CAB、CCAB等方法存在的主特征向量误判的问题。该方法不需要系统提供波束指向和估计目标信号方向、具有对系统误差的稳健性，能够较好地抑制干扰。模拟实验验证了本文的方法。