幅值比约束下稀布阵的旁瓣抑制混合算法

针对非均匀稀布圆环阵的旁瓣抑制问题提出了一种基于粒子群算法和二阶锥规划算法的混合算法。该混合算法结合两种算法的优势,将粒子群算法作为全局搜索器进行阵元位置的优化,二阶锥规划算法作为局部搜索器进行阵元权值的优化,能够获取较低的峰值旁瓣电平。该算法同时引入相邻阵元最小间距的约束,优化了算法的搜索空间,提高了寻优效率。最后,考虑到阵列天线系统的可实现性,给出了动态幅值比约束下的混合算法。与粒子群算法和参考文献方法的对比实验结果表明:本文算法可进一步降低稀布圆环阵的旁瓣电平,仿真数据验证了算法的有效性和天线系统的可实现性。     

基于二阶锥规划的相干信号深零陷自适应波束形成

针对常规自适应波束形成算法在强相干干扰情况下零陷深度不够、甚至干扰抑制失效的问题,提出了一种基于二阶锥规划的相干信号深零陷自适应波束形成算法。该算法首先对接收数据协方差矩阵进行Toeplitz重构,使其包含信号和干扰的所有方位信息。然后重构了干扰加噪声协方差矩阵。接着在保证期望方向波束无失真前提下,约束主瓣宽度和旁瓣电平,使得波束形成器干扰和噪声的输出功率最小。最后将该问题转化为凸优化中的二阶锥规划问题,并使用凸优化工具箱进行快速求解。仿真结果验证了该算法的有效性。     

Robust Least Squares Constant Modulus Algorithm to Signal Steering Vector Mismatches

The linearly constrained least squares constant modulus algorithm (LSCMA) may suffer significant performance degradation and lack robustness in the presence of the slight mismatches between the actual and assumed signal steering vectors, which can cause the serious problem of desired signal cancellation. To account for the mismatches, we propose a doubly constrained robust LSCMA based on explicit modeling of uncertainty in the desired signal array response and data covariance matrix, which provides robustness against pointing errors and random perturbations in detector parameters. Our algorithm optimizes the worst-case performance by minimizing the output SINR while maintaining a distortionless response for the worst-case signal steering vector. The weight vector can be optimized by the partial Taylor-series expansion and Lagrange multiplier method, and the optimal value of the Lagrange multiplier is iteratively derived based on the known level of uncertainty in the signal DOA. The proposed implementation based on iterative minimization eliminates the covariance matrix inversion estimation at a comparable cost with that of the existing LSCMA. We present a theoretical analysis of our proposed algorithm in terms of convergence, SINR performance, array beampattern gain, and complexity cost in the presence of random steering vector mismatches. In contrast to the linearly constrained LSCMA, the proposed algorithm provides excellent robustness against the signal steering vector mismatches, yields improved signal capture performance, has superior performance on SINR improvement, and enhances the array system performance under random perturbations in sensor parameters. The on-line implementation and significant SINR enhancement support the practicability of the proposed algorithm. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement compared with linearly constrained LSCMA.     

基于L型和差嵌套阵的二维波达方向估计

针对传统L型均匀阵列二维波达方向（Direction of Arrival,DOA）估计中可估计信源数目受限于阵元数、分辨率低等问题,提出了一种新的L型和差嵌套阵列结构。该L型阵列的两个子阵布置相同,是非均匀的稀疏阵,通过阵元位置之间的差分、求和操作达到虚拟扩展阵元数目的效果,从而提升阵列的自由度。采用该阵列进行二维DOA估计时,两个子阵分别先进行一维的DOA估计,再采用PSCM（Pair-matching Signal Covariance Matrices）算法进行一维角度配对。每个子阵进行一维波达方向估计时,先采用VCAM（Vectorized Conjugate Augmented MUSIC）算法生成非均匀稀疏阵的求和求差协方差矩阵,再采用矩阵重构的方法恢复协方差矩阵的秩,最后对协方差矩阵采用MUSIC（Multiple Signal Classification）算法进行DOA估计。实验仿真表明,本阵列有着更高的自由度和估计精度。      

基于混合三角变异差分进化算法的平面稀疏阵列约束优化

针对旁瓣零陷凹面约束的稀疏平面阵列优化及算法早熟等问题,该文基于参数自适应的思想,提出一种混合三角变异差分进化算法。通过引入旁瓣零陷凹面约束矩阵,构建自适应惩罚函数,时变权重组合变异策略与交叉策略,提高算法前期全局搜索能力和后期收敛能力,最终实现峰值旁瓣电平和旁瓣零陷凹面的平面阵列约束优化。仿真结果表明,对比混合三角变异策略前的算法,该算法在完成稀疏阵列峰值旁瓣电平优化的同时,能在指定旁瓣区域完成零陷凹面设计,降低有源干扰影响。     

基于谱分析的稳健自适应波束形成算法

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下降问题,提出一种基于谱分析的稳健自适应波束形成(SA-RAB)算法。算法利用空域与频域的对称性,根据真实导向矢量与理想导向矢量之间的误差,运用谱分析(SA)技术确定波束主瓣宽度,最后利用二阶锥规划(SOCP)技术在主瓣宽度内形成平顶响应,并在副瓣区域内进行干扰抑制。仿真结果表明:该算法可有效地抑制干扰,并输出理想的信号干扰噪声比(SINR),且提高了波束形成针对导向矢量误差的稳健性,验证了算法的有效性和优越性。     

Performance of adaptive array antenna with arbitrary geometry in the presence of mutual coupling

The effect of the mutual coupling between the array elements on the performance of the adaptive array antennas (AAA) is investigated when the actual received voltages which include the mutual coupling are directly used to estimate the weight vector based on the adaptive algorithm. The output signal-to-interference-noise ratio (SINR), the convergence of the adaptive algorithm and the synthesized pattern are evaluated to study the effect due to the existence of the mutual coupling. It is found that the mutual coupling affects the antenna adaptive gain, but does not affect the adaptive processing. It is also found that the mutual coupling does not always degrade the iterative convergence of the adaptive algorithm. It is proved that any invertible matrix for compensating the mutual coupling cannot improve the output SINR. It is also indicated that the radiation pattern can be correctly synthesized in the presence of the mutual coupling by introducing the universal steering vector (USV) whose element corresponds to the array element pattern.     

基于L型阵列酉变换矩阵重构的二维DOA估计

二维空间信号波达方向（DOA）的估计是阵列信号处理的一个关键研究问题。经典的二维 MUSIC算法固然精度高,但此算法需要二维谱峰搜索,运算较为复杂。提出一种用于L型阵列的二维DOA估计算法,通过矩阵重构使得阵列输出矩阵变为中心对称矩阵,再利用酉变换矩阵将其由复值矩阵变为实值矩阵。该方法可以直接得到目标参数,不需要谱峰搜索,使得运算量大大降低。相比于 L 型阵列适用的增广矩阵束（MEMP）算法,该算法可以估计更多信源的DOA,并能获得较高的分辨率。计算机仿真结果表明,该算法具有较高的DOA估计精度。     

迭代子空间跟踪和结构约束的自适应波束形成算法

自适应波束形成(adaptive beam-forming:ABF)方法通过对阵列数据进行加权处理来获得最大的输出信干噪比,对采样协方差矩阵依赖性较大.在小快拍和通道随机响应误差条件下,采样协方差矩阵的估计值与真实值通常存在较大差异,严重恶化了基于线性约束最小均方误差准则的自适应波束形成方法的干扰抑制性能.针对该问题,本文基于子空间投影类波束形成方法的思想,提出采用迭代子空间跟踪和结构约束的自适应波束形成方法.该方法首先利用clearing技术在训练样本集上依次跟踪主特征矢量并构成信号子空间;然后根据子空间投影类波束形成权矢量的结构特性计算自适应加权矢量.仿真结果表明本文方法能有效提高自适应波束算法的输出信干噪比.     

Robust Adaptive Beamformers Based on Worst-Case Optimization and Constraints on Magnitude Response

In this paper, novel robust adaptive beamformers are proposed with constraints on array magnitude response. With the transformation from the array output power and the magnitude response to linear functions of the autocorrelation sequence of the array weight, the optimization of an adaptive beamformer, which is often described as a quadratic optimization problem in conventional beamforming methods, is then reformulated as a linear programming (LP) problem. Unlike conventional robust beamformers, the proposed method is able to flexibly control the robust response region with specified beamwidth and response ripple. In practice, an array has many imperfections besides steering direction error. In order to make the adaptive beamformer robust against all kinds of imperfections, worst-case optimization is exploited to reconstruct the robust beamformer. By minimizing array output power with the existence of the worst-case array imperfections, the robust beamforming can be expressed as a second-order cone programming (SOCP) problem. The resultant beamformer possesses superior robustness against arbitrary array imperfections. With the proposed methods, a large robust response region and a high signal-to-interference-plus-noise ratio (SINR) enhancement can be achieved readily. Simple implementation, flexible performance control, as well as significant SINR enhancement, support the practicability of the proposed methods.     

Analysis of modified SMI method for adaptive array weight control

The authors characterize the performance of the diagonally loaded sample matrix inverse (SMI) algorithm versus the number K of snapshots used in the covariance matrix estimate by providing O(1/K) statistics (bias and variance) of the array weights, output powers, and output power ratios such as signal to interference noise ratio (SINR) and INR. The approach accommodates wideband signals. Monte Carlo simulations verify the theoretical analysis     

一种基于自适应阵列天线的波束赋形算法

自适应阵列天线中的数字波束赋形(DBF)技术是智能天线数字信号处理部分的核心.提出了一种可用于自适应阵列波束赋形的SMI-LMS算法--由SMI(采样协方差矩阵求逆)算法决定LMS(最小均方)算法的初始权向量.该算法充分结合了SMI算法收敛速度快和LMS算法稳态误差小的优点,能在较强干扰环境下,确保权向量的快速收敛和跟踪速度.与传统的LMS算法相比,SMI-LMS算法具有良好的收敛性能、较快的跟踪速度和较小的输出误差,并可以有效改善自适应方向图的副瓣性能.仿真结果验证了该结论.     

基于主奇异矢量的L型阵列相干信号二维DOA估计方法

针对现有L型阵列相干信号DOA估计算法精度不高、孔径损失较大的问题,该文提出一种基于主奇异矢量的解相干(L-PUMA)方法以及改进的主奇异矢量法(L-MPUMA)。L-PUMA算法首先对互协方差矩阵进行降噪,再通过奇异值分解得到2维主奇异矢量,然后利用加权最小二乘法得到线性预测方程的多项式系数,该线性预测方程的根即为信号的DOA估计,最后提出一种新的配对算法实现仰角和方位角的配对。L-MPUMA算法利用反向共轭变换构造增广主奇异矢量,进一步提高了数据利用率,克服了信号完全相干时L-PUMA算法性能下降严重的问题,仿真实验验证了所提算法的高效性。     

基于粒子群优化的数字波束形成算法

波束形成的加权系数求解是一个优化过程,现有算法大多经过多次迭代,计算量大,实现复杂。为降低波束形成算法复杂度,将粒子群优化原理应用于数字波束形成中,提出了基于粒子群优化的自适应数字波束形成算法。该算法将每一组权值作为一个粒子,将阵列加权和的输出信号与干扰噪声比(SINR)作为适应度函数,通过比较各个粒子的适应度值,进行迭代搜索得到最优解。该算法可使天线阵在主波束对准有用信号,同时能有效抑制两个以上的干扰,且对阵列通道误差有较好的稳健性。计算机仿真验证了算法的有效性。     

Effect of mutual coupling on the performance of adaptive arrays

The effect of mutual coupling between array elements on the performance of adaptive arrays is examined. The study includes both steady state and transient performance. An expression for the steady state output signal-to-interference-plus-noise ratio (SINR) of adaptive arrays, taking into account the mutual coupling between the array elements, is derived. The expression is used to assess the steady state performance of adaptive arrays. The transient response is studied by computing the eigenvalues associated with the signal covariance matrix. The steering vector required to maximize the output SINR of Applebaum-type adaptive arrays in the presence of mutual coupling is also given.     

The maximin algorithm for adaptive arrays and frequency-hopping communications

An algorithm is described for maximizing of the signal-to-interference-plus-noise ratio (SINR) at the output of an adaptive array. The algorithm discriminates between the desired signal and any interference on the basis of the spectral characteristics of frequency-hopping signals. The algorithm maximizes the SINR by maximizing the desired.signal output power while simultaneously minimizing the interference-plus-noise output power. Rapid convergence of the weights to the steady-state values is achieved by the adjustment of the loop gain as a function of the estimated SINR at each iteration. Implementation of the algorithm in conjunction with a frequency-hopping modem is explained. Computer simulation results describe both the steady-state and transient responses of the system under various jamming conditions. The results indicate a rapid dynamic response and the steering of deep nulls over a wide range of the SINR.     

CDMA中小扩频增益下波束形成

针对CDMA系统中扩频增益较小和用户数较多时信号阵列响应估计精度较差的问题,提出了利用基站收到的码元序列与解扩后的接收序列通过加窗构造新序列来进行波束权值估计的新方法。理论分析与仿真结果表明,该方法能够减少阵列响应的估计对扩频增益和功率控制的敏感性,当用户数大于15时能使输出SINR比现有的码滤波方法平均提高2．5dB。     

一种二维信号波达方向估计的改进多重信号分类算法

多重信号分类(MUSIC)算法是一种经典的空间谱估计算法。该文以L型阵列为例,针对2D-MUSIC算法在接收信号信噪比较小时对多个目标中方位相近的目标无法进行准确估计的问题,提出一种改进2D-MUSIC算法。该算法对经典2D-MUSIC算法所构成的协方差矩阵进行共轭重组,并将重组后矩阵的平方与原协方差矩阵的平方进行相加求平均,由此获得新的矩阵,再对该矩阵对应的噪声子空间进行加权处理,选取适当的加权系数构造新的噪声子空间,最后通过谱峰搜索识别出目标位置。计算机仿真结果表明,与2D-MUSIC算法相比,改进后的算法在接收信号信噪比较小时对多个目标中方位相近的目标也能够进行信号波达方向(DOA)估计,提高了L型阵列2维DOA估计的分辨率,具有较好的工程应用价值。     

一种自动匹配的分布式非圆信号二维DOA快速估计方法

在相干分布式非圆信号2维波达方向(DOA)估计中,针对利用非圆特性后维数扩展带来的较大复杂度问题,且现有的低复杂度算法均需要额外的参数匹配,该文提出一种基于互相关传播算子的自动匹配2维DOA快速估计算法。该算法考虑L型阵列,在建立相干分布式非圆信号扩展阵列模型的基础上,首先证明了L阵中两个子阵的广义方向矢量(GSV)均具有近似旋转不变特性,然后通过阵列输出信号的互相关运算消除了额外噪声,最终利用子阵GSV的近似旋转不变关系通过传播算子方法得到中心方位角与俯仰角估计。理论分析和仿真实验表明,所提算法无须谱峰搜索和协方差矩阵特征分解运算,具有较低的计算复杂度,并且能够实现2维DOA估计的自动匹配;同时,相比于现有的相干分布式非圆信号传播算子算法,所提算法以较小的复杂度代价获得了性能的较大提升。     

Convergence of the SMI and the diagonally loaded SMI algorithmswith weak interference [adaptive array]

Approximations for the power levels at the output of an adaptive array that uses the diagonally loaded sample matrix inversion (SMI) algorithm are derived. Diagonal loading is a technique where the diagonal of the covariance matrix is augmented with a positive or negative constant prior to inversion. The authors examine how the signal-to-interference-plus-noise ratio (SINR) and signal-to-interference ratio (SIR) at the array output vary with the number of samples taken when the input signals are continuous wave. It is shown that positive loading produces more rapid convergence with a reduction in output SIR. Negative loading provides an improved SIR level, but it is shown that positive loading produces more rapid convergence with a reduction in output SIR. Negative loading provides an improved SIR level, but it is shown that the output power levels are erratic and slow to converge. Simulation results which verify the theoretical procedure are given