有源干扰条件下机载MIMO雷达降维STAP方法

针对机载多输入多输出雷达杂波与干扰的同时抑制问题,提出一种降维空时自适应处理方法.首先,利用系统参数离线简便构造出杂波子空间,然后,根据杂波基向量与干扰基向量的关系,对杂波加干扰协方差矩阵的秩进行分析.当存在有源干扰时,系统的杂波与干扰自由度进一步提高.最后利用干扰加噪声协方差矩阵的块对角结构,结合离线构造的杂波子空间,通过迫零方法进行空时自适应权矢量求解.该方法能够有效降低运算复杂度,具有接近全维最优处理的杂波与干扰抑制性能.     

基于空时二维协方差矩阵修正的波束形成算法

在实际应用环境中,信源和阵列传感器等存在误差,假设期望信号的导向矢量与真实信源导向矢量的失配会导致阵列波束形成器把期望信号当作干扰来加以抑制。针对信号匹配误差导致自适应波束形成性能下降的问题,提出了一种基于空时二维协方差矩阵修正的波束形成算法,利用空时结构对宽带幅相误差校正的特性,对空时二维协方差矩阵进行重构,并对修正协方差矩阵进行特征值分解,分离出信号加干扰子空间,将失配导向矢量投影可使期望信号与噪声子空间严格正交,最后求解算法最优权值。算法有效改善了波束形成的输出信噪比,计算机仿真验证了理论分析的正确性和算法的稳健性。     

基于子空间跟踪的DSSS通信系统抗窄带干扰研究

对接收信号自相关矩阵分析表明,窄带干扰的能量集中在一个低秩子空间(干扰子空间)内,扩频信号和噪声的能量均匀散布在整个特征空间,通过跟踪接收信号自相关矩阵大特征值对应特征矢量构成的干扰子空间,可实现对窄带干扰的有效抑制。文中采用RRLS算法跟踪干扰子空间,并根据干扰子空间最小特征值与噪声功率的比值自动确定干扰子空间的维数。分析和仿真表明子空间跟踪窄带干扰抑制算法实现复杂度低,数值稳定性好,性能优于传统的自适应横向滤波器。     

一种快速的特征空间自适应波束形成算法

特征空间自适应波束形成（EBAB）算法的权向量是线性约束最小方差（LCMV）波束形成器的权向量在信号干扰子空间上的投影,因此需要计算量巨大的特征分解。基于信号干扰子空间投影矩阵可利用阵列协方差矩阵进行高精度的有理近似的思想,提出一种快速计算自适应权向量的算法。理论分析和计算机仿真表明,在信号干扰子空间维数已知时,该算法的性能同EBAB算法一致,同时该算法对子空间维数过估计不敏感。     

基于协方差矩阵重构和导向矢量估计的稳健自适应波束形成

实际应用中, 当假定的与真实的期望信号导向矢量之间存在一定误差时, 波束形成器的性能会急剧下降, 特别是当期望信号功率很强的时候.为解决这个问题, 提出了一种新的算法.当信源数小于阵元数时, 干扰加噪声协方差矩阵具有稀疏性.新方法首先利用该特性重构干扰加噪声协方差矩阵并由此得到与干扰导向矢量正交的子空间, 使接收的数据通过该子空间得到只含有期望信号和噪声的混合信号, 然后,对该混合信号基于最大化输出功率原理估计期望信号导向矢量, 最后,把得到的导向矢量和正交子空间来构造阵列加权值.仿真结果表明:该算法分别在假定的期望信号导向矢量存在误差、期望信号很强和低快拍数时仍然具有良好的性能.     

一种基于广义旁瓣相消的稳健降维方法

在自适应阵列信号处理中,为了高效进行干扰和噪声对消,提出了一种基于广义旁瓣相消的稳健降维方法。该方法利用信号子空间特征矢量和期望信号的投影导向矢量构造稳健降维阻塞矩阵,阻塞期望信号和噪声分量,使辅助支路中只含有干扰信号,达到了降维的效果,同时提高了算法对阵列天线误差的稳健性。仿真结果表明,该方法对阵列模型误差不敏感,可以有效地降低运算时间。     

一种基于正交加权约束的GPS抗干扰盲算法

针对GPS信号C/A码的周期重复特性,联合CAB算法和子空间技术提出了一种盲自适应波束形成方法。该算法通过将CAB算法的权矢量和控制矢量约束到干扰导向矢量的正交补空间内,使之在期望信号方向上产生高的增益,并在干扰方向上形成零陷。该方法具有盲处理、收敛速度快和低计算复杂度等优点。仿真结果证明了算法的有效性。     

用于双卫星导航系统用户接收机抗干扰的自适应 干扰正交约束下的最佳信号增强算法

本文提出了一种适用于双星用户机的自适应干扰正交约束下的最佳信号增强算法,该算法将干扰抑制和最佳双星信号接收问题,转化为确定干扰正交子空间和无干扰加权矢量,以及无干扰加权矢量向干扰正交子空间投影的问题.为使小口径的CRPA形成分别对准两颗卫星的波束,本文采用基于等信噪比最佳信号增强算法计算无干扰加权矢量;而为得到干扰正交子空间,本文引入MUSIC算法,通过分析样点数目和通道的不一致性对最小特征值的影响,给出了划分干扰子空间和干扰正交子空间的门限.所提算法干扰抑制比大,输出电平稳定,权矢量更新速度可达μs级,并能保证对两颗卫星一定的增益.     

一种低复杂度的信号子空间拟合的新方法

提出一种低复杂度的信号子空间拟合的新方法.证明了多级维纳滤波器的匹配滤波器(或降维矩阵的列矢量)可以张成一个压缩信号子空间.利用其与Krylov子空间等效这一特点,推导出信号子空间拟合一个新的基本公式,进而建立信号子空间拟合一个新的准则函数.分析表明,压缩信号子空间可以由降维矩阵的列矢量有效地张成,而且计算降维矩阵只需要多级维纳滤波器的若干步前向递推,所以本文方法的运算量和复杂度均较小.最后,计算机仿真验证了本文方法的有效性.     

一种新的GPS接收机宽带干扰抑制方法

该文针对GPS扩频接收机空时自适应处理结构,提出了一种新的极大抑制干扰的波束形成算法。通过估计空时二维功率谱得到各干扰信号的导向矢量矩阵,并求解出该矩阵的一组最接近期望信号导向矢量的正交基,作为空时二维最优权值。仿真结果表明,该算法增强了空时自适应结构方向图的零陷深度,比传统宽带多线性约束LCMV算法更有效地进行干扰抑制,明显提高了信号干扰噪声比。     

Interference suppression via orthogonal projections: a performanceanalysis

Several recent studies indicate the promise of subspace separation principles when applied to adaptive jammer suppression in phased array antennas. This paper theoretically analyzes the performance of a subspace separation technique based on orthogonal projections (OP) for adaptively suppressing interference in phased arrays; the theoretical performance predictions are validated using computer simulations. This analysis holds for the case when it is possible to differentiate between the vector spaces spanned by jammers and additive noise. The performance parameters used are (a) the average residual interference (jammer plus noise) power at the output of the adapted array as a function of the number of jammer snapshots used for calculating the weight vector, and (b) the similarity of the adapted array pattern to the design pattern away from the jammer locations. The performance of the OP-based subspace separation technique is compared with the sample matrix inversion (SMI) algorithm. It is shown that the weight vector calculated using OP converges more quickly to the optimal solution (infinite number of interference snapshots) than the SMI weight vector. Further, in contrast to the SMI adapted pattern, which exhibits large sidelobe levels away from the jammer locations, the OP adapted pattern closely follows the design pattern both in the mainbeam and in the sidelobe region away from the jammer locations     

Wideband LFM Interference Suppression Based on Fractional Fourier Transform and Projection Techniques

A novel wideband LFM interference suppression method is proposed based on fractional Fourier transform (FRFT) and projection techniques. By using the concentration property of LFM interferences in fractional Fourier domain, the initial frequency, frequency modulation rate and direction-of-arrival (DOA) of LFM interferences are first estimated. Afterwards, the space–time interferences subspace is constructed. In order to suppress wideband LFM interferences, the received signal is projected onto the subspace orthogonal to the interferences subspace when the signals are uncorrelated. Furthermore, considering the scenario when the signals are coherent, according to the properties of oblique projection, LFM interferences are suppressed by projecting the received signal onto the joint signal–noise subspace. Then, the desired signal is extracted by beamforming. Unlike the existing algorithms, the proposed method not only can exactly estimate parameters and directly solve the DOA of LFM interferences but also can construct the interferences subspace with the time-variant steering vector of interferences. Moreover, our method can effectively suppress LFM interferences without reducing the freedom, and significantly improve the performance, even at low signal-to-noise ratio (SNR) or small number of snapshots. Simulation results demonstrate the effectiveness of the proposed method.     

Beampattern Synthesis via a Matrix Approach for Signal Power Estimation

We present new beampattern synthesis approaches based on semidefinite relaxation (SDR) for signal power estimation. The conventional approaches use weight vectors at the array output for beampattern synthesis, which we refer to as the vector approaches (VA). Instead of this, we use weight matrices at the array output, which leads to matrix approaches (MA). We consider several versions of MA, including a (data) adaptive MA (AMA), as well as several data-independent MA designs. For all of these MA designs, globally optimal solutions can be determined efficiently due to the convex optimization formulations obtained by SDR. Numerical examples as well as theoretical evidence are presented to show that the optimal weight matrix obtained via SDR has few dominant eigenvalues, and often only one. When the number of dominant eigenvalues of the optimal weight matrix is equal to one, MA reduces to VA, and the main advantage offered by SDR in this case is to determine the globally optimal solution efficiently. Moreover, we show that the AMA allows for strict control of main-beam shape and peak sidelobe level while retaining the capability of adaptively nulling strong interferences and jammers. Numerical examples are also used to demonstrate that better beampattern designs can be achieved via the data-independent MA than via its VA counterpart.     

基于响应矢量优化的共形阵列稳健波束形成方法

针对共形阵列天线自适应波束形成中存在的主瓣保形困难、旁瓣电平高等问题,该文提出基于波束响应矢量优化的自适应波束形成方法。在主瓣波束保形约束条件下,通过自适应地调整波束响应矢量,求得最优的响应矢量,进而求得准最优自适应权。该方法将非凸的二次约束二阶优化问题转化到高维空间进行求解,并通过半正定松弛(SDR)转化为凸优化问题,高效求得准最优解。该方法不仅保持了期望的主瓣响应,同时克服了传统线性约束最小方差(LCMV)自适应波束形成方法旁瓣高的缺点,并且对阵列构型具有稳健性。仿真实验验证了该方法的有效性。     

一种虚拟波束形成自适应加权空间平滑算法

该文提出了一种用于自适应阵列的自适应加权空间平滑算法。通过构造虚拟自适应波束形成问题求取用于子阵协方差矩阵加权的加权向量,然后进行加权空间平滑自适应波束形成。理论分析与仿真结果表明,新算法能更有效地降低期望信号和干扰之间的相关性,使得用于空间平滑的子阵数减少,以降低阵列孔径损失。     

一种降维的MIMO雷达MVDR波束形成算法

深入研究了多输入多输出（Multiple-Input Multiple-Output, MIMO）雷达的波束形成问题,针对MIMO雷达直接运用常规的最小方差无失真响应（MVDR）波束形成算法时所需的训练样本数大和运算复杂度高的不足,提出了一种降维的MVDR波束形成算法。该算法首先将1个 维（ 分别为发射和接收阵元数）的权矢量分解成两个低维（1个 维和1个 维）权矢量的Kronecker积,然后分别利用MVDR算法求得分解后的两个权矢量,最后再合成原始的最佳权矢量。该算法通过将一个高维权矢量的求解问题转换成两个低维权矢量的分别求解问题,在保证波束形成器性能的基础上有效地降低了运算复杂度,减少了所需的训练样本数,因此本文算法更具有实际应用价值。仿真结果验证了算法的有效性。      

Interference mitigation in STAP using the two-dimensional Wold decomposition model

We propose a novel parametric approach for modeling, estimation, and detection in space-time adaptive processing (STAP) radar systems. The proposed parametric interference mitigation procedures can be applied even when information in only a single range gate is available, thus achieving high performance gain when the data in the different range gates cannot be assumed stationary. The model is based on the Wold-like decomposition of two-dimensional (2D) random fields. It is first shown that the same parametric model that results from the 2D Wold-like orthogonal decomposition naturally arises as the physical model in the problem of space-time processing of airborne radar data. We exploit this correspondence to derive computationally efficient fully adaptive and partially adaptive detection algorithms. Having estimated the models of the noise and interference components of the field, the estimated parameters are substituted into the parametric expression of the interference-plus-noise covariance matrix. Hence, an estimate of the fully adaptive weight vector is obtained, and a corresponding test is derived. Moreover, we prove that it is sufficient to estimate only the spectral support parameters of each interference component in order to obtain a projection matrix onto the subspace orthogonal to the interference subspace. The resulting partially adaptive detector is simple to implement, as only a very small number of unknown parameters need to be estimated, rather than the field covariance matrix. The performance of the proposed methods is illustrated using numerical examples.     

Speaker identification based on adaptive discriminative vector quantisation

A novel adaptive discriminative vector quantisation technique for speaker identification (ADVQSI) is introduced. In the training mode of ADVQSI, for each speaker, the speech feature vector space is divided into a number of subspaces. The feature space segmentation is based on the difference between the probability distribution of the speech feature vectors from each speaker and that from all speakers in the speaker identification (SI) group. Then, an optimal discriminative weight, which represents the subspace's role in SI, is calculated for each subspace of each speaker by employing adaptive techniques. The largest template differences between speakers in the SI group are achieved by using optimal discriminative weights. In the testing mode of ADVQSI, discriminative weighted average vector quantisation (VQ) distortions are used for SI decisions. The performance of ADVQSI is analysed and tested experimentally. The experimental results confirm the performance improvement employing the proposed technique in comparison with existing VQ techniques for SI and recently reported discriminative VQ techniques for SI (DVQSI)     

相干环境下LCMV自适应阵列抗干扰问题研究

相干干扰环境下,自适应阵列性能会急剧下降,常用的处理方法是空间平滑。然而,采用常规均匀空间平滑的自适应阵列对相干干扰的抑制能力较差,而且会损失阵列孔径。该文提出一种改进的相干干扰抑制方案:首先,提出了一种自适应的加权空间平滑(Weighted Spatial Smoothing, WSS)算法,它将各子阵的相关矩阵进行加权平均,可以最大程度地解相干;然后,在WSS基础上,利用线性约束最小方差(LCMV)准则得到子阵波束形成器最佳权矢量;最后,提出了一种利用子阵间的相位关系对全阵做波束形成的方法。该方案大大提高了阵列对抗相干干扰的能力,同时避免了一般空间平滑后阵列的孔径损失。理论分析和仿真结果说明了方法的有效性和稳健性。