一种多路并行盲搜索的空时二维抗干扰算法

针对卫星导航抗干扰中传统的空时自适应处理算法运算量大、处理速度较慢的问题,在介绍多级维纳滤波基本原理的基础上,提出了一种多路并行的盲搜索空时二维抗干扰自适应处理算法。该方法采用多级维纳滤波降维处理的方法求解信号子空间和干扰子空间,从而使输出信号的信干噪比达到最优,实现干扰的有效抑制。仿真结果表明,该算法具有较好的抗干扰性能,且运算量较低,有较好的工程应用前景。     

一种稳健恒定束宽宽波束形成算法

传统宽带波束形成算法在导向矢量失配时输出性能下降,为解决该问题,文章提出一种稳健恒定束宽波束形成算法。该算法首先构造与快拍数相关的对角加载函数;其次,基于空域积分思想,结合入射信号的方向误差范围估计期望信号的实际入射方向,并结合构造的对角加载系数生成优化波束加权系数;最后,联合优化后的波束权值与FIR滤波器系数完成宽带波束响应的全局优化设计。经仿真实验验证,相比于传统分布设计法的时域宽带波束形成,文中方法可以得到较理想的恒定宽带主瓣响应和较低的旁瓣电平,同时在期望信号导向矢量失配情况下,仍有较好的输出信干噪比。     

凸集优化稳健自适应DBF 的在线算法

一种基于凸集优化稳健DBF 的在线算法,以克服平稳和非平稳误差引起的阵列性能下降。算法利用球体约束和 权值约束,建立凸集最优目标函数,采用最速下降法确定对角负载因子,并迭代求解最优权矢量。仿真结果表明,与传 统的capon 算法和固定对角负载值算法相比,基于凸集优化的迭代算法具有更大的阵列输出信干噪比,从而使阵列具有 更好的稳健性。     

迭代对角加载采样矩阵求逆鲁棒自适应波束形成

为有效克服导向矢量大失配误差对自适应波束形成器的影响,该文提出了一种迭代对角加载采样矩阵求逆鲁棒自适应波束形成算法。该算法对传统对角加载算法进行了迭代运算,基于Capon波束形成器的最优权矢量与假定导向矢量的基本关系,将每一步得到的权矢量,对应反解出一个比导向矢量假定值更为准确的导向矢量,并替代假定值,最终逼近真实的期望信号导向矢量。提出的方法在迭代过程中只需一步递推,无需对导向矢量建立不确定集,避免了在每步迭代中运用拉格朗日数值法或凸优化法,且明显提高了波束形成器的输出信干噪比。仿真结果验证了算法的正确性和有效性。     

基于数据选择Householder多级维纳滤波导航接收机空时抗干扰方法

针对抑制GPS信号中期望信号方向的冲激毛刺干扰影响问题,对Householder多级维纳滤波方法进行改进,提出一种数据选择Householder多级维纳滤波器.改进方法用于GPS信号空时抗干扰处理中,可以避免期望信号方向的冲激毛刺干扰对最优权值计算的影响,同时保持期望信号不受影响.仿真结果验证了算法良好的抗干扰性能.     

对角加载对信号源数检测性能的改善

对角加载技术可抑制小特征值对自适应权值的影响来加速自适应波束形成器的收敛性以及抑制导向矢量误差的影响避免信号相消,该技术通常用于稳健的波束形成算法.基于对角加载技术,本文提出了一种信号源数目判定的改进方法,通过对角加载数据协方差阵,可以平滑小快拍数和空间色噪声时的噪声特征值分散程度从而减轻其对信号源数目估计的影响,证明了该估计器的强一致性,分析了加载量对信号源数目估计的影响.最后通过仿真以及实测数据比较了本文方法和已有方法的性能,验证了所提方法的有效性.     

一种改进的MUSIC方法

多级维纳滤波是一种降维算法,文中将多级维纳滤波引入到多重信号分类(MUSIC)算法中,提出了适合于MUSIC方法的多级维纳滤波器结构。该方法避免了采样数据协方差矩阵的特征值分解,降低了运算量。仿真结果证明了方法的有效性。     

基于功率估计的高动态GNSS抗干扰零陷展宽算法

全球卫星导航系统(GNSS)接收机在高速运动状态下时,干扰来向的快速变化会导致阵列抗干扰算法性能下降,为此,提出一种基于功率估计的抗干扰零陷展宽算法。根据特征值将采样协方差矩阵划分为信号子空间与噪声子空间,通过子空间投影确立信号导向矢量与功率的线性关系;利用线性关系估计干扰功率,并根据零陷展宽需求重新设定干扰区域内的信号功率;最后,以干扰区域内功率的估计值为基础重构干扰加噪声协方差矩阵,求解阵列权矢量。仿真表明,相比其他零陷展宽算法,所提算法在相同展宽下具有更深的零陷,阵列输出信干噪比也有所提升。     

多级维纳降秩频移滤波器在直扩系统中的抗干扰应用

直扩系统中,采用频移滤波器作为白化滤波器,利用了窄带干扰的循环平稳特性,可得到比传统线性时不变滤波器更好的抗干扰效果.本文把多级维纳滤波算法应用到频移滤波器中.理论分析和仿真表明,利用多级维纳滤波算法,频移白化滤波器在低秩条件下,仍可以得到很好的抗干扰效果.     

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扰抑制.与传统对角加载类稳健波束形成算法相比,所提算法不受对角加载因子的影响,且在信干噪比较大时仍然具有良好的抗干扰性能.仿真对比实验验证了所提算法的有效性.     

Performance of reduced-rank linear interference suppression

The performance of reduced-rank linear filtering is studied for the suppression of multiple-access interference. A reduced-rank filter resides in a lower dimensional space, relative to the full-rank filter, which enables faster convergence and tracking. We evaluate the large system output signal-to-interference plus noise ratio (SINR) as a function of filter rank D for the multistage Wiener filter (MSWF) presented by Goldstein and Reed. The large system limit is defined by letting the number of users K and the number of dimensions N tend to infinity with K/N fixed. For the case where all users are received with the same power, the reduced-rank SINR converges to the full-rank SINR as a continued fraction. An important conclusion from this analysis is that the rank D needed to achieve a desired output SINR does not scale with system size. Numerical results show that D=8 is sufficient to achieve near-full-rank performance even under heavy loads (K/N=1). We also evaluate the large system output SINR for other reduced-rank methods, namely, principal components and cross-spectral, which are based on an eigendecomposition of the input covariance matrix, and partial despreading. For those methods, the large system limit lets D→∞ with D/N fixed. Our results show that for large systems, the MSWF allows a dramatic reduction in rank relative to the other techniques considered     

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     

一种多级维纳滤波器的快速实现算法——迭代相关相减算法

证明了由相关相减算法实现的多级维纳滤波器是一种酉多级维纳滤波器,并且其各级的匹配滤波器与对应的降秩子空间的基向量相等。在证明结论的基础上,将后向迭代算法应用于相关相减算法多级维纳滤波器,提出了多级维纳滤波器的迭代相关相减实现算法。迭代相关相减算法具有前向递推计算量低、后向滤波实时性好、采样均方误差和自适应权矢量计算简单、秩选计算量低等优点,降秩性能与相关相减算法完全相同。     

基于可变对角载入的鲁棒自适应波束形成算法

针对传统算法对方向向量偏差敏感的缺点,提出了一种基于可变对角载入的鲁棒自适应波束形成算法.为了提高算法的鲁棒性,采用非线性约束条件下的最优化阵列输出功率对信号方向向量进行优化求解,且优化解中的参量能够准确求出.为了减少计算量,采用递推算法求逆矩阵并利用泰勒级数展开,推导出基于可变对角载入的权重向量公式.该算法可有效地抑制方向向量偏差所带来的影响,降低了计算量易于实时实现,提高了系统的鲁棒性,改善了阵列输出的信干噪比,使其更接近最优值.仿真结果表明,该算法相对传统算法可以获得更好的性能.     

Large system transient analysis of adaptive least squares filtering

The performance of adaptive least squares (LS) filtering is analyzed for the suppression of multiple-access interference. Both full-rank LS filters and reduced-rank LS filters, which reside in a lower dimensional Krylov space, are considered with training, and without training but with known signature for the desired user. We compute the large system limit of output signal-to-interference-plus-noise ratio (SINR) as a function of normalized observations, load, and noise level. Specifically, the number of users K, the degrees of freedom N, and the number of training symbols or observations i all tend to infinity with fixed ratios K/N and i/N. Our results account for an arbitrary power distribution over the users, data windowing (e.g., recursive LS (RLS) with exponential windowing), and initial diagonal loading of the covariance matrix to prevent ill-conditioning. Numerical results show that the large system analysis accurately predicts the simulated convergence performance of the algorithms considered with moderate degrees of freedom (typically N=32). Given a fixed, short training length, the relative performance of full- and reduced-rank filters depends on the selected rank and diagonal loading. With an optimized diagonal loading factor, the performance of full- and reduced-rank filters are similar. However, full-rank performance is generally much more sensitive to the choice of diagonal loading factor than reduced-rank performance.     

Side-lobe constrained beamforming undervirtual expansion of L-shaped array

To reduce the side-lobe level of L-shaped expansion array and improve the output signal to interference and noise ratio (SINR), the algorithm of side-lobe constraint based on minimum variance distortionless response ( MVDR- SC) is proposed. Firstly, the approach of mixing diagonal loading and Mailloux-Zatman (DLMZ) is used to taper the covariance matrix of the expansion array. Then, the second order cone programming ( SOCP) obtained by constructing a new matrix is used to control the beam side-lobe. Finally, the new adaptive weight numbers are constructed by adjusting the proportion between DLMZ and SOCP. Simulation results show that the MVDR-SC algorithm can effectively reduce the side-lobe of beamforming under the L-shaped expansion array and obtain a larger output SINR. At the same time, it has good robustness to the mutual coupling error.     

一种相干信号自适应波束形成零陷展宽算法

针对传统自适应波束形成器在相干干扰位置出现快速变化时,输出性能下降,甚至干扰抑制失效的问题,提出了一种相干信号自适应波束形成零陷展宽算法。首先,对接收数据协方差矩阵进行解相干处理得到Toeplitz矩阵;其次,对协方差矩阵进行重构和优化;最后,进行零陷展宽和对角加载处理。仿真结果表明：该算法不仅能对相干干扰自适应形成宽零陷,而且可以灵活地控制零陷的深度和宽度;与Mailloux算法相比,在输入信噪比较高的情况下,该算法能得到更低的波束旁瓣电平和更高的输出信干噪比     

柔性稀疏阵的稳健波束形成

本文将导向矢量失配时的稳健波束形成问题归结为二次锥规划问题,利用高效的内点法求解。该波束形成器成功地应用于存在阵元位置误差的柔性稀疏阵,相对于经典的对角线加载法、特征空间法,在不同的输入信噪比下获得了更好的输出信号干扰加噪声比。仿真结果表明对超稀疏分布的柔性阵,阵元位置误差对输出SINR起决定性影响,而阵列稀疏程度对其影响不大。     

基于凸优化方法的认知雷达低峰均比波形设计

为了提高雷达发射波形的检测性能,同时使发射机发挥其最大效能,以发射波形的低峰均比(PAR)为约束条件,该文提出了一种信号相关杂波背景下的认知雷达发射波形和接收机滤波器联合优化方法。首先,面向距离扩展目标检测问题,构建关于雷达输出信干噪比(SINR)的优化模型;然后将该模型转化为Rayleigh商形式,给出了接收机权值的解析表达式;在此基础上,通过半正定松弛,将关于发射波形半正定矩阵的非凸问题转化为凸问题,求得发射波形的最优矩阵解;最后,将秩1近似法和最近邻方法相结合,从最优矩阵解中提取出发射波形的最优向量解。该方法在给定PAR取值范围内可使波形的输出SINR达到最大,PAR=2时波形的SINR值与能量约束下优化波形的SINR值相同,并且比PAR=1时所得波形高出约0.5 dB。仿真结果验证了所提方法的有效性。      

Rank-2-Optimal Adaptive Design of Binary Spreading Codes

Over the real/complex field, the spreading code that maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear filter is the minimum-eigenvalue eigenvector of the interference autocovariance matrix. In the context of binary spreading codes, the maximization problem is NP-hard with complexity exponential in the code length. A new method for the optimization of binary spreading codes under a rank-2 approximation of the inverse interference autocovariance matrix is presented where the rank-2-optimal binary code is obtained in lower than quadratic complexity. Significant SINR performance improvement is demonstrated over the common binary hard-limited eigenvector design which is shown to be equivalent to the rank-1-optimal solution.