Efficient robust adaptive beamforming for cyclostationary signals

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the LS-SCORE algorithm presented in a paper by Agee et al. (1990) has been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, this algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the LS-SCORE algorithm in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. An efficient method is then developed based on the fact that the array output power approaches its maximum as the CFE is reduced. We formulate the problem as an optimization problem for reducing the CFE iteratively to achieve robust adaptive beamforming against the CFE. Simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are provided     

Robust adaptive array beamforming for cyclostationary signals undercycle frequency error

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the SCORE algorithms presented by Agee, Schell and Gardner (1990) have been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, these algorithms suffer from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the SCORE algorithms in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. Based on the theoretical result, we then develop an efficient method in conjunction with the SCORE algorithms to achieve robust adaptive beamforming against the CFE. Several simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are also presented     

修正主模抑制稳健自适应波束形成算法

针对自适应波束形成器在少快拍和波束指向角度失配情况下性能严重下降的问题,该文提出一种基于修正协方差矩阵的稳健主模抑制算法。算法提出利用修正协方差矩阵估计主模成分,进而重构出数据协方差矩阵,同时利用其特征向量向期望信号导引向量的投影来确定信号子空间维数。为提高算法在低信噪比情况下的性能,提出利用信号区域积分得到正定矩阵,以其主特征向量对信号子空间进行扩充,再将导引向量向信号子空间进行投影得到修正的导引向量。最终利用重构协方差矩阵及修正导引向量得到最优权值。理论分析和仿真实验验证了所提算法的有效性和稳健性。     

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.     

多普勒信号的稳健盲自适应波束形成

针对假设的多普勒频率和真实的多普勒频率之间的误差会导致盲波束形成的性能急剧的下降这一事实. 首先分析了存在多普勒误差时的最小均方误差准则意义下的盲自适应波束形成的性能, 然后提出了一种有效的算法来对抗多普勒误差, 该算法是一迭代过程, 可以在线估计出真实的多普勒频率. 计算机仿真验证了提出算法的有效性.     

基于投影对消处理技术的稳健自适应波束形成

针对传统自适应波束形成算法中目标波达方向(Direction of Arrival,DOA)估计不准确引起的波束形成性能下降问题,提出了一种采用投影对消矩阵的稳健自适应波束形成算法.首先,寻找与估计波达方向有最大相关性的特征矢量作为目标信号特征矢量,然后构建对消矩阵消除协方差矩阵中的信号分量,最后通过增加零点约束实现干...     

基于特征值斜投影的主瓣干扰抑制方法

主瓣干扰将导致传统自适应波束形成算法的主瓣畸变、峰值偏移、旁瓣电平抬升,致使相控阵系统抗干扰性能严重恶化.另外,当接收快拍中存在目标信号时,会导致自适应波束形成在目标信号方向生成零陷,降低接收目标信号增益.针对这些问题,本文提出一种基于特征值斜投影的主瓣干扰抑制方法.首先通过构建特征值斜投影预处理矩阵滤除主瓣干扰,然后...     

CFE条件下基于循环维纳滤波的单通道分离算法

在航天测控数传（C＆T）信号中,频谱混叠的现象非常普遍,因此对频谱严重混叠的信号进行单通道盲分离成为信号处理领域中研究的热点和难点。基于线性-共轭-线性频移（LCL-FRESH）滤波的基本概念,考虑到在实际非合作通信应用中基于循环平稳的LCL-FRESH滤波依赖于较高的循环频率精度,然而循环频率误差（CFE）总是无可避免,提出了一种改进的CFE校正算法,简单分析了误差条件下滤波器性能下降的原因。仿真表明,所研究的算法可以有效分离存在CFE下时频重叠的数传通信信号。     

基于提升Keystone变换的声呐宽带自适应波束形成方法

针对Keystone变换在宽带阵列预处理方面的优势和常规Keystone变换存在的阵元数据缺失问题,该文将自回归模型与常规Keystone变换相结合,提出一种基于提升Keystone变换的声呐宽带自适应波束形成算法。该算法首先将常规Keystone变换应用于宽带阵列信号的相位对齐,接着采用自回归模型对变换后各频段缺失的阵元数据进行预测补偿,最后通过稳健自适应波束形成处理获得目标方位输出结果。仿真实验结果表明,基于提升Keystone变换的宽带自适应波束形成算法性能优于常规Keystone自适应算法、指向最小方差自适应算法和聚焦自适应算法。

     

Wideband Blind Source Separation Algorithm Based on Beamforming

As the problem of array mixing model of wideband signals cannot be solved by conventional blind source separation algorithms, an improved algorithm based on beamforming is proposed in this paper. First, the received signals are transformed into time–frequency domain, and the delays of source signals are estimated. Then, the received signals are compensated with the estimated delay in frequency domain. Finally, the desired signal is acquired by using Frost wideband beamforming algorithm. Due to adopting the new methods of single source point extraction and delay estimation, the complexity of the proposed algorithm is reduced. Pre-steering delay is used in frequency domain to eliminate the compensation error when the delay is not an integer multiple of the sampling interval, which improves the separation performance significantly. The simulation results show that the proposed algorithm can adequately solve the problem of delay mismatch and achieve wideband blind source separation effectively. The existing algorithms are mostly fail for frequency hopping signals when there are numerous overlapping time–frequency points. In this case, the proposed algorithm still has good separation performance.

     

基于区域约束的双耳近场自适应波束形成算法

提出了一种基于区域约束的双耳近场自适应波束形成算法，该算法将目标信号及其附近区域内的若干个导向矢量组成的矩阵进行特征值分解，并在最小化输出信号能量的同时对主要的特征向量进行约束，从而有效解决传统近场波束形成器对目标声源的方位估计误差和位置扰动过于敏感的问题。实验结果表明，所提方法可以有效抑制远场同向干扰声源，并且相对于传统近场波束形成器具有更高的鲁棒性。客观评价指标表明，该算法的降噪性能及语音质量提升方面均优于对比算法。      

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.     

一种零陷展宽鲁棒自适应波束形成算法

自适应波束形成器对模型失配与运动干扰非常敏感,特别是训练数据中包含期望信号分量时,较小的系统误差也会导致算法性能严重下降。该文提出一种零陷展宽鲁棒自适应波束形成算法,通过重构和优化干扰加噪声协方差矩阵,并估计真实导向矢量来提高算法抗系统误差与运动干扰的鲁棒性。该算法还可以通过调整主瓣宽度来优化波束旁瓣。仿真实验验证了所提算法的正确性和有效性。     

Robust adaptive beamforming for general-rank signal models

The performance of adaptive beamforming methods is known to degrade severely in the presence of even small mismatches between the actual and presumed array responses to the desired signal. Such mismatches may frequently occur in practical situations because of violation of underlying assumptions on the environment, sources, or sensor array. This is especially true when the desired signal components are present in the beamformer "training" data snapshots because in this case, the adaptive array performance is very sensitive to array and model imperfections. The similar phenomenon of performance degradation can occur even when the array response to the desired signal is known exactly, but the training sample size is small. We propose a new powerful approach to robust adaptive beamforming in the presence of unknown arbitrary-type mismatches of the desired signal array response. Our approach is developed for the most general case of an arbitrary dimension of the desired signal subspace and is applicable to both the rank-one (point source) and higher rank (scattered source/fluctuating wavefront) desired signal models. The proposed robust adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization. Simple closed-form solutions to the considered robust adaptive beamforming problems are derived. Our new beamformers have a computational complexity comparable with that of the traditional adaptive beamforming algorithms, while, at the same time, offer a significantly improved robustness and faster convergence rates.     

近场条件下数字阵列雷达自适应波束形成技术研究

针对近场条件下数字阵列雷达导向矢量幅相非一致性对自适应波束形成（adaptive beamforming,ADBF）算法性能的影响,通过构建近场多通道数字阵列雷达回波信号模型,分析近场多通道信号二维频谱,发现在近场条件下带限干扰信号的频谱会出现非均匀分布,呈现周期性栅格分布特征,造成算法性能下降.本文提出一种具有全新干扰样本选择策略的近场ADBF(near field ADBF, NF-ADBF)算法,通过寻优干扰信号频谱栅格边界,在栅格区间进行多门限样本筛选,离散提取干扰信号样本,构建完备的干扰信号协方差矩阵,提升近场条件下的自适应处理性能.通过在地面搭建仿真试验环境,模拟典型的数字阵列近场工作环境,通过录取试验数据分析并与理论仿真进行对比,验证了近场干扰样本筛选策略和NF-ADBF算法模型的有效性.     

基于分数低阶统计量的线性约束恒模盲自适应波束形成方法

为解决传统恒模波束形成算法在受冲击噪声污染的无线环境中的性能退化问题,本文提出基于分数低阶统计量的线性约束恒模盲自适应波束形成方法,并从理论上分析了算法收敛条件。仿真实验表明,在稳定分布噪声下,新方法具有比传统恒模波束形成方法更好的韧性,而且比线性约束最小平均P范数波束形成方法具有更高的输出信干噪比,即使在强脉冲干扰环境下新方法仍具有较好的信号恢复能力。     

基于波束域导向矢量估计的稳健自适应波束形成方法

针对导向矢量失配和样本协方差矩阵估计误差导致自适应波束形成性能下降的问题,该文提出一种基于波束域导向矢量估计的稳健方法。该方法首先利用期望信号角度失配区域的补集构造波束域转换矩阵,克服了当样本协方差矩阵中存在期望信号分量时现有算法出现有用信号对消的缺点。其次推导出波束域导向矢量估计方法,并将其转化为包含恒模约束的非凸二次约束二次规划问题,然后采用半正定松弛规划估计实际的波束域导向矢量。计算机仿真验证了该文所提方法的有效性。     

一种稳健的稀疏Capon波束形成算法

常规Capon波束形成算法具有相对较高的旁瓣增益,且在期望信号导向矢量存在失配时,阵列输出性能下降甚至失效。为解决这一问题,引入了稀疏约束Capon波束形成算法,该算法降低了旁瓣,对期望信号来向不确定具有一定稳健性,但在幅相误差、期望信号指向偏差等多种误差同时存在的情况下其性能下降。本文在稀疏约束Capon波束形成算法基础上,给出了一种稳健的稀疏Capon波束形成算法。该算法主要是在最差性能最优化的情况下,在稀疏Capon上增加了一个导向矢量存在偏差的约束条件。通过计算机仿真,验证了新算法在多种误差环境下的有效性与优越性。     

基于矩阵优化理论的自适应数字波束形成算法研究

自适应数字波束形成作为天线技术和数字信号处理融合的产物,近年来成为跨领域的研究热点,并且广泛应用到新一代的相控阵雷达和移动通信中。文中提出一种基于矩阵流形理论的自适应波束形成算法。不同于传统的波束形成算法,该算法将波束形成问题转化为高维优化问题,并引入矩阵流形优化理论,在Stiefel流形上求解该问题。由于降低了求解问题的维度,限制了最优解的范围,该算法相比于传统的自适应波束形成算法,具有收敛速度快、运算量小、鲁棒性好的优点。     

双基地MIMO雷达发射波束形成与多目标定位

针对在导向矢量存在误差情况下自适应波束形成算法性能严重下降的问题, 提出一种基于导向矢量估计的鲁棒自适应波束形成(Steering Vector Estimation Based Robust Adaptive Beamforming, SVE-RAB)算法.算法用导向矢量不确定范围估计真实导向矢量, 利用范数约束通过二阶锥规划技术提高波束形成的鲁棒性.算法可在导向矢量存在误差的情况下, 对期望信号保持最大增益并有效抑制干扰, 且有效提高了波束形成输出的信干噪比(Signal to Interference plus Noise Ratio, SINR).仿真结果验证了算法的有效性和优越性.