存在相干信号时的最优波束形成

本文提出一种新的存在相干信号时的最优波束形成方法。该方法首先利用估计得到的期望信号和相干信号 的方向形成变换矩阵,去掉数据中的期望信号和相干信号成分，求得不相关干扰信号的子空间以及其正交子空间，然后得到期望信号和相干信号的合成导向矢量在该正交子空间中的投影矢量，并把该投影矢量作为自适应权矢量。经理论分析表明，这种方法基本上和理论上的最优方法相同。另外，该方法可以适用于任意的阵列结构，并且对期望信号和相干信号方向估计误差具有很强的稳健性。计算机仿真结果证实了本文方法的有效性。     

基于均匀圆阵相干信源的二维DOA估计

随着通信技术的不断发展,信号传输环境变得日益复杂。针对多径传播形成的高度相关和相干信号测向问题,提出了一种基于均匀圆阵相干信源的二维DOA估计方法。该方法利用均匀圆阵轴向虚拟平移解相干,通过去噪后利用虚拟子阵的自协方差矩阵和互协方差矩阵构造波达方向矩阵,利用该矩阵特征分解估计信号的俯仰角;然后将平滑后的自协方差矩阵与波束空间变换矩阵相乘,使圆阵的导向矢量具备范德蒙结构,最后用求根MUSIC算法估计出信号的方位角,完成了相干信号的二维DOA估计。该方法无需二维谱峰搜索,方位角和俯仰角自动配对,计算量小,分辨率高。仿真实验证明了所提方法的正确性。     

电磁矢量传感器阵列相干和独立信号DOA估计

本文研究利用矢量传感器阵列接收数据的高阶累计量区分独立和相干信号,进行参数估计.从矢量阵列信号高阶累积量任意数量的切片中估计多信号中的一个独立信号参数时,利用隔离向量选通目标信号,对其他信号进行隔离,提高了独立信号参数估计性能,信号源中可包含相干信号.独立信号的电场导向矢量与电磁导向矢量因垂直而内积为零,利用此关系可得到已虑除了所有独立信号的多个四阶累积量切片矩阵,相干信号存在于矩阵的信号子空间中.滤除独立信号估计相干信号参数,可提高相干信号参数估计性能.因可抑制任意方向,任意数量的独立干扰,即能抗饱和干扰,算法可作为电子对抗工具.仿真试验表明该方法有效.     

一种存在相干信号时的稳健的最优波束形成算法

提出了一种新的存在相干信号时的最优波束形成算法。该方法首先利用二阶统计量信息估计合成方向矩阵,然后给出了一种根据期望信号的波迭方向寻找与之对应的合成方向矢量的方法．再据此估计最优权矢量。该方法与现有的同类算法相比计算量更小、鲁棒性更强。计算机仿真证明了算法的性能。     

基于IAA的协方差矩阵重构稳健波束形成方法

针对小样本背景下,存在相干信号、相位及阵元位置误差,传统波束形成方法性能不佳的问题,提出了基于迭代自适应法(IAA)的协方差矩阵重构稳健波束形成方法。该方法利用IAA估计出精确的功率谱,并进一步利用IAA估计的功率重构干扰协方差矩阵。重构过程中,将积分区域缩小到三维立体环域,减少无用信息的影响,提高了干扰协方差矩阵的重构精确度。最后通过波束形成抑制干扰信号。由于IAA不依赖于信号的非相干假设,解决了相干信号存在下的方位估计和功率估计。仿真表明,所提出的方法在相干信号、少快拍、相位及阵元位置误差同时存在的情况下,相对于其他波束形成方法,具有最优的信干噪比(SINR)输出,表明该方法具有优良的抑制干扰性能。     

导航系统的通道自均衡空时抗干扰技术

针对导航系统由于通道之间幅相特性的不一致导致抗干扰性能下降的问题,本文提出了一种通道自均衡的空时抗干扰技术。该方法首先在无干扰的情况下,利用本地参考信号与接收数据的相关特性求出实际导向矢量,再采用波达方向估计方法估计出信号的理想波达方向,进而求出理想导向矢量,并由此实际导向矢量和理想导向矢量求得幅相误差矩阵,然后在有干扰的情况下,利用求得的幅相误差矩阵对接收数据进行阵列误差校正,再对校正后的数据进行波束形成,此方法可以改善通道失配导致干扰抑制性能下降的问题,仿真验证了其有效性和优越性。     

基于导向矢量估计的鲁棒波束形成

针对标准Capon波束形成器中真实导向矢量与期望导向矢量存在误差时,其性能会急剧下降的问题,提出了基于加权空间平滑与导向矢量估计相结合的鲁棒波束形成算法。该算法利用加权空间平滑方法,对子阵进行特殊的划分,根据子阵间自相关矩阵与互相关矩阵权重差异,采用嵌套的方式获得加权矩阵,继而得到更加精确的协方差矩阵,接着,使用不确定范围约束期望导向矢量来获得真实导向矢量。仿真结果表明,和传统的自适应波束形成算法相比较,本文算法在面对协方差矩阵中含有期望信号以及角度失配问题时,鲁棒性得到明显提升。     

基于多级阻塞的稳健相干自适应波束形成

针对期望信号波达角(DOA)估计误差较大时相干波束形成性能下降的问题,该文提出一种基于多级阻塞的稳健相干自适应波束形成算法。该算法首先定义阻塞矩阵,推导多级阻塞原理,并利用其滤除阵列接收信号中的期望信号;然后给出空间中只存在期望信号时,子阵与全阵间阵列流型的映射关系,据此推导全阵扩展变换,并证明其在干扰信号存在条件下的有效性;最终利用扩展变换获取全阵最优权矢量,实现相干波束形成。该算法对期望信号波达角估计误差稳健,且无需干扰信号来向的先验信息,同时可以有效避免阵列孔径的损失。仿真分析验证了算法的优越性和理论分析的有效性。     

基于稳健Capon波束形成技术的矢量相关测向方法

为了弥补阵列天线导向矢量失配对测向性能的影响,提出基于稳健Capon波束形成技术的矢量相关测向方法。与直接使用相位差的常规相关干涉测向技术不同,该方法首先利用稳健Capon波束形成技术估计目标信号的真实导向矢量;然后通过导向矢量的相关拟合确定目标信号方向。通过仿真分析,得出了以测向为衡量标准时不确定集约束参数的选择原则。仿真结果表明该方法能够弥补阵列流型失配的影响、准确测量目标信号方向。     

一种基于降秩变换的ESB改进算法

提出了一种基于降秩变换的ESB改进算法,该方法首先用降秩变换矩阵对阵列接收数据进行降秩变换,利用干扰子空间和修正的期望信号导向矢量来构造降秩变换矩阵,然后进行波束形成.该算法解决了传统的基于特征空间(ESB)自适应波束形成算法方向图副瓣电平较高的问题,并且解决了当阵列协方差矩阵中不含期望信号或期望信号较小时方向图畸变的问题,从而大大提高了抗干扰能力.仿真结果证明,所提出的基于降秩变换的ESB改进算法具有良好的波束形成性能,验证了算法的有效性.     

基于特征空间的相干干扰抑制技术

该文把多线性约束和特征空间技术相结合,提出了基于特征空间的多约束最小方差波束形成器(EM-CMVB),该波束形成器可以有效抑制相干干扰。EMCMVB是利用估计得到的相干干扰方向和阵列信号相关矩阵特征分解来得到修改的信号子空间,再把多约束最小方差波束形成器(MCMVB)的权矢量向修改的信号子空间投影来得出新的权矢量。经分析表明,EMCMVB的性能优于MCMVB的性能。最后,给出了计算机仿真结果,证实了EMCMVB的优越性。     

Outage Probabilities of Wireless Systems with LCMV Beamforming

This paper investigates the outage probability of a wireless system with linear constrained minimum variance (LCMV) beamforming using a uniform linear array beamformer. LCMV beamforming is able to perfectly cancel a number of dominant interferers while other interferers remain. A simplified beamforming model is used to derive closed-form outage probability expressions considering the impact of LCMV beam patterns on various interferers. Fading statistics of Rayleigh, Rician, and Nakagami are used to characterize the desired signal, whereas interferers are assumed to be subject to Rayleigh fading. One important aspect of this paper is the consideration of the directions of arrivals (DOA) of the dominant interferers and the exact beam patterns in the outage performance evaluations of LCMV beamforming systems. Numerical results of the outage probability are presented to illustrate the impact of DOA's of the dominant interferers and the impact of different fading scenarios. The paper also presents performance comparison between LCMV beamforming and conventional beamforming considering different interference scenarios (DOA's of dominant interferers).     

Effect of pointing errors on the performance of the projectionbeamformer

The projection beamformer generates a steering constraint vector for beamforming by projecting the original steering vector onto the signal-plus-interference subspace of the correlation matrix. This work investigates the effect of pointing errors on the performance of the projection beamformer. A general expression for the output SINR is first derived for the projection beamformer with multiple interferers present. The expression can also be applied to the directional-constraint beamformer except that the steering constraint vector is replaced by the steering vector. Based on the expression for the output SINR, the sensitivity of the projection and the directional-constraint beamformers to pointing errors are examined for the cases of no interferer and a single interferer. Moreover, the performance of the projection beamformer with the source number overestimated is evaluated. Theoretical analysis validated by computer simulations indicates that the projection beamformer performs better than the directional-constraint beamformer, even when the source number is overestimated     

DOA Estimation of Coherent Signals Based on Coherent Accumulation Vector

The direction-of-arrival (DOA) method based on multiple Toeplitz matrices reconstruction (MTOEP) method that employs all rows of the array output covariance matrix to reconstruct the Toeplitz matrix, which can effectively resolve coherent signals. However, the existing MTOEP method suffers serious performance degradation when in low signal-to noise (SNR) owing to the square transformation of noise energy. In this paper, a modified MTOEP method based on coherent accumulation processing is proposed. Firstly, because the strong coherence of the narrowband signal and the uncorrelated noise at adjacent snapshots, the accumulation vector is obtained via performing coherent accumulation processing on multiple snapshots. Then, the Toeplitz matrix can be reconstructed by exploiting the coherent accumulation vector and the received data from all sensors, which improves the signal energy, and further results in the improvement of the SNR. Finally, the DOAs can be estimated via the subspace-based methods. Theoretical analysis and simulation results show that compared with the existing MTOEP method, the proposed method provides an improved DOA estimation accuracy and a higher probability of successful resolution at low SNR.

     

混合信号情况下信源数与DOA联合估计算法

针对非相关信源与相干信源共存情况,提出了一种基于矩阵重构的信源数与波达方向（direction of arrival,DOA）联合估计算法.该算法首先利用特征值的二阶统计量（second order statistic of eigenvalues,SORTE）法和子空间旋转不变技术（estimated signal parameter via rotational invariance techniques,ESPRIT）实现非相关信源数与DOA估计;然后基于空间差分法消除非相关信号并构造新矩阵,利用构造矩阵进行前向空间平滑,实现对相干信源解相干;最后利用SORTE法检测相干信源数,结合求根多重信号分类（multiple signal classification,MUSIC）算法估计相干信源DOA.与传统的差分平滑方法相比,该算法在可估计信源数与低信噪比情况下DOA估计性能等方面优于传统算法.数值仿真实验结果验证了该算法的有效性.     

Blind IB-MSNR Beamforming for DSSS-based Communication Systems

The spread spectrum technique is widely used and effective in combating narrowband interference with a moderate power as well as co-channel interference (CCI) and noise. However, it requires a configuration with long signature sequence for interference suppression, which is practically impossible. In addition, a system will exhibit a substantial degradation in performance due to both strong interference and desired coherent signals introduced by multipaths. In this paper, a novel detector is first proposed to determine the number of strong interferers for direct sequence spread spectrum (DSSS) based communication systems. Furthermore, the blind interference-blocked (IB) maximum signal-to-noise ratio (MSNR) beamforming is then proposed to collect desired coherent signals and to suppress strong interference. Theoretical analysis and numerical results demonstrate that the proposed IB-MSNR beamformer can achieve nearly the same performance as the optimal non-blind beamformer.     

Two-dimensional direction of arrival estimation in the presence of uncorrelated and coherent signals

In this paper, a novel two-dimensional direction of arrival (2-D DOA) estimation method is proposed based on a new array configuration when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are estimated using the non-zero eigenvalues and corresponding eigenvectors of the DOA matrix (DOAM) combined with our proposed criterion. Meanwhile, we can form a new matrix without the information of uncorrelated signals. Then the coherent signals are resolved with the redefined DOAM that is constructed by the smoothed matrices of the new matrix. Simulation results demonstrate the effectiveness and efficiency of the proposed method. Other arrays that contain multiple identical central-symmetric subarrays (e.g. uniform rectangular arrays) can also be applied with our method.     

Quadratically constrained adaptive beamforming for coherent signalsand interference

Severe signal cancellation often occurs in conventional adaptive beamforming if coherent interferers are present. The paper proposes adding quadratic constraints to a linearly constrained minimum variance (LCMV) adaptive beamformer to prevent signal cancellation in coherent interference environments. The additional constraint limits the beamformer output mean squared error to be less than a specified level. It is shown that if the quadratic constraint is properly chosen, then the original linearly constrained beamformer's signal cancellation is reduced to an arbitrarily small level while the interference cancellation is unchanged. In practice, the quadratic constraint is constructed based on estimates of the interference parameters. Sensitivity analyses show the performance of the resulting beamformer is robust with respect to this approximate constraint. The effectiveness of the quadratically constrained adaptive beamformer is further illustrated through simulations     

Generalized eigenspace-based beamformers

The generalized eigenspace-based beamformer (GEIB) is presented here, which utilizes the eigenstructure of the correlation matrix to enhance the performance of the linearly constrained minimum variance beamformer (LCMVB). The weight vector of the GEIB is found by projecting the LCMVB weight vector onto a vector subspace constructed from the eigenstructure of the correlation matrix. The GEIB and the LCMVB have the same responses to the desired signal and the interferers. However, the weight vector of the GEIB has a smaller norm and generates a lower output noise power. An additional advantage of the GEIB is that the linear constraints can be treated flexibly, i.e. each linear constraint can be chosen to be preserved or not preserved. The cost of preserving a linear constraint is to get more output noise power. In addition to developing the GEIB, we discuss the effects of imposing linear constraints on the output noise powers of the GEIB and the LCMVB. Computer simulations are also presented that demonstrate the merits of the GEIB