干扰抑制技术在通信侦察中的应用

介绍了两种用于智能天线的抑制干扰波束形成算法,与传统的Capon波束形成算法相比,介绍的算法不仅能够在干扰信号方向形成零点,并且还可以在指定方向形成零点,从而更好地提高接收信号的信干比。从稳健性而言,介绍的两种算法要高于传统的Capon波束形成算法。     

盲信号分离算法研究

介绍了一种基于快速定点的盲分离算法，该算法可以对来自不同方向上的统计独立信号进行有效的分离，并且不需要预先知道信号的测向和阵列的结构流形，因此该算法称为盲分离算法。另外该算法不仅有传统的波束形成的优点，即在有用信号方向形成主波束，并且还能够在干扰方向形成很好的零点，所以该算法是波束形成技术和零点形成技术的结合，可大大提高信干比，另外该算法还有收敛快的特点，对于每一个信号大约迭代10次后就可以收敛。     

基于盲信号分离的高分辨测向算法研究

文章首先介绍了一种基于快速定点的盲分离算法，该算法可以对来自不同方向上的统计独立信号进行有效的分离，并且不需要预先知道信号的方向和阵列的结构流形，因此该算法被称为盲分离算法。该算法不仅有传统的波束形成的优点，即在有用信号方向形成主波束，并且还能够在干扰方向形成很好的零点，所以该算法是波束形成技术和零点形成技术的结合，可大大提高信干比。在盲分离的基础上，文章提出了一种高分辨。测向算法，该算法在高信噪比的情况下，具有与MUSIC、ESPRIT和最大似然算法相似的分辨率，而在低信噪比的情况下，体现了该算法的优越性，具有很好的分辨率。     

基于编码发射与自适应波束形成的超声成像

与传统的延时叠加(DAS)波束形成的成像方法相比,Capon算法可以有效地提高医学超声成像的横向分辨率,但不能提高成像的对比度。该文提出一种新的成像方法Chirp_Capon算法,即将超声编码发射技术与Capon算法相结合,利用编码信号优异的相关特性来弥补Capon算法在对比度上的不足,从而得到了较好的成像结果。仿真结果表明相对于Capon算法,该算法不仅具有较高横向分辨率,而且可以有效提高图像的对比度和信噪比。     

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。     

一种稳健接收波束形成算法

接收波束形成是阵列信号处理中的重要部分.经典Capon波束形成算法虽然可以形成理想波束,但需要精确获知信号的到达方向(DOA).在实际应用中,由于模型失配可能会造成DOA失配.因此提出了一种非退化椭圆约束条件下的稳健波束形成算法,允许在出现DOA失配情况下依然可以有效地接收信号,同时利用仿真验证了算法的可靠性.     

改进的稳健Capon 波束形成算法性能分析

常规Capon 波束形成算法能够使波束在期望信号方向形成高增益,在干扰方向形成零陷,针对该算法在期望信号导向矢量失配的情况下,出现性能下降的问题,研究了期望信号导向矢量在不确定集约束下的求解。通过分析稳健Capon波束形成算法的特点,推导出了期望信号导向矢量在球形不确定集约束下的权矢量近似闭式解,并采用图像法,找到给定条件下的最优约束参数。在指向误差和相位误差存在情况下,对算法进行了仿真分析,仿真结果验证了算法在误差存在情况下的稳健性。     

阵列误差对Capon波束形成算法的影响分析

针对阵列误差存在时经典(Capon)波束形成算法的性能严重恶化问题,介绍了均匀圆阵阵列流型下的Capon波束形成算法,对阵列天线各阵元间的幅度误差、相位误差、水平位置误差和垂直位置误差进行了数学建模,仿真分析了各种阵列误差对Capon波束形成算法的影响,并给出了该影响的量化分析结果,从而对该算法工程实现时的指标确定提供依据。     

基于稳健最小二乘的鲁棒波束形成

为解决Capon波束形成器在存在导向矢量失配时的性能急剧下降问题,提出了一种结合广义旁瓣对消器和稳健最小二乘的鲁棒波束形成算法.该算法利用广义旁瓣对消器原理将Capon波束形成器转化为最小二乘问题,然后在数据协方差矩阵误差的范数约束下将其转化为二阶锥规划问题,并利用高效内点法得到最优解.所提出的算法经推导证明属于对角加载类.仿真分析表明,该算法在导向矢量失配和快拍不足时仍具有较好的性能.     

TD-SCDMA系统中智能天线波束形成的研究

波束形成算法是智能天线的核心和理论基础,TD-SCDMA系统中应用的智能天线技术与传统的切换波束技术相比,最大的优点在于可以自适应的根据用户的方位调整波束方向,将主波束对准来波方向(DOA),而将旁瓣和零陷对准干扰.文中先阐述了智能天线的基本原理,介绍了Capon算法和MUSIC算法以及它们在智能天线波束形成中的应用,进而用Matlab进行了数值仿真,并且讨论了相关参数设置对于算法性能的分析.文章为以后的理论研究提供了平台,对于今后的理论研究有一定的指导意义和参考价值.     

On robust Capon beamforming and diagonal loading

The Capon (1969) beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. We show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches, but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance for SOI power estimation is demonstrated via a number of numerical examples.     

Performance analysis of forward-backward matched-filterbankspectral estimators

The problem of complex spectral estimation is of great interest in many applications. This paper studies the general class of the forward-backward matched-filterbank (MAFI) spectral estimators including the widely used Capon as well as the more recently introduced amplitude and phase estimation of a sinusoid (APES) methods. In particular, we show by means of a higher order expansion technique that the one-dimensional (1-D) Capon estimator underestimates the true spectrum, whereas the 1-D APES method is unbiased; we also show that the bias of the forward-backward Capon is half that of the forward-only Capon (to within a second-order approximation). Furthermore. We show that these results can be extended to the two-dimensional (2-D) Capon and APES estimators. Numerical examples are also presented to demonstrate quantitatively the properties of and the relation between these MAFI estimators     

Asymptotic Performance Analysis of Blind Minimum Output Energy Receivers for Large DS-CDMA Systems

The random matrix theory is used to analyze the asymptotic performance of the blind minimum output energy (MOE) receiver in direct-sequence code division multiple-access (DS-CDMA) systems in the presence of unknown multipath channel under the condition that the spreading factor and the number of users go to infinity with the same rate. As a special case, the asymptotic properties of the blind Capon receiver are also studied and the conditions of convergence of the signal-to-interference-plus-noise ratio (SINR) of this receiver to that of the optimal minimum-mean-square error (MMSE) receiver are discussed. In particular, it is shown that the SINR performances of the Capon and MMSE receivers are nearly identical in the uplink scenario, while the performance of the Capon receiver may be considerably inferior to that of the MMSE receiver in the downlink transmission case. As the performance of the Capon receiver is closely related to the performance of the Capon channel estimator, the asymptotic properties of the latter estimator are also studied and the conditions of convergence of the Capon channel estimate to a scaled version of the channel vector of the user-of-interest are obtained.     

Capon estimation of covariance sequences

Estimating the covariance sequence of a wide-sense stationary process is of fundamental importance in digital signal processing (DSP). A new method, which makes use of Fourier inversion of the Capon spectral estimates and is referred to as theCapon method, is presented in this paper. It is shown that the Capon power spectral density (PSD) estimator yields an equivalent autoregressive (AR) or autoregressive moving-average (ARMA) process; hence, theexact covariance sequence corresponsing to the Capon spectrum can be computed in a rather convenient way. Also, without much accuracy loss, the computation can be significantly reduced via an approximate Capon method that utilizes the fast Fourier transform (FFT). Using a variety of ARMA signals, we show that Capon covariance estimates are generally better than standard sample covariance estimates and can be used to improve performances in DSP applications that are critically dependent on the accuracy of the covariance sequence estimates.This work was supported in part by National Science Foundation Grant MIP-9308302, Advanced Research Project Agency Grant MDA-972-93-1-0015, the Senior Individual Grant Program of the Swedish Foundation for Strategic Research and the Swedish Research Council for Engineering Sciences (TFR).     

A generalized capon estimator for localization of multiple spread sources

In this correspondence, we develop a generalized Capon spatial spectrum estimator for localization of multiple incoherently distributed (spread) sources in sensor arrays. The proposed generalized Capon technique estimates the source central angles and angular spreads by means of a two-dimensional (2-D) parameter search. Simulation results show that the proposed method has a substantially improved performance compared with several popular spread source localization methods.     

Multi-dimensional Capon spectral estimation using discrete Zhang neural networks

The minimum variance spectral estimator, also known as the Capon spectral estimator, is a high resolution spectral estimator used extensively in practice. In this paper, we derive a novel implementation of a very computationally demanding matched filter-bank based a spectral estimator, namely the multi-dimensional Capon spectral estimator. To avoid the direct computation of the inverse covariance matrix used to estimate the Capon spectrum which can be computationally very expensive, particularly when the dimension of the matrix is large, we propose to use the discrete Zhang neural network for the online covariance matrix inversion. The computational complexity of the proposed algorithm for one-dimensional (1-D), as well as for two-dimensional (2-D) and three-dimensional (3-D) data sequences is lower when a parallel implementation is used.     

一种联合修正的稳健Capon波束形成算法

指出了水平定向天线阵波束形成的主要难点,没有固定相位中心和受交叉极化来波的影响。阵列受随机性误差使得导向矢量存在较大失配,从而导致传统Capon算法性能下降甚至失效。在阵列误差模型下,给出了基于协方差矩阵与导向矢量联合修正的稳健Capon波束形成算法。该算法首先基于收缩得到一个增强的协方差矩阵,然后通过最大化Capon输出功率实现对导向矢量的修正,同时增加二次型约束防止修正的导向矢量接近于干扰导向矢量上。该算法可转化为二次约束二阶规划问题,并通过凸优化进行求解。仿真结果表明,该算法对天线阵模型中误差矩阵具有一定的稳健性,且较其他稳健算法具有较好的性能。