恒模特性在DOA中的新应用及其克拉美-罗界研究

研究了恒模特性和DOA估计之间的联系，基于最小二乘恒模和权向量多项式求根提出一种简单有效的LSCM-DOA估计新算法，同时为分析此DOA估计算法的性能，详细推导了恒模信号在任意阵型下的克拉美-罗界。理论分析和实际仿真证明该算法几乎达到其克拉美-罗界，而且性能相对于未应用恒模特性的MUSIC和ESPRIT等算法较优越，尤其在小快拍数和低信噪比情况下取得了良好的效果，并且因为未进行谱峰搜索而将计算复杂度控制在较低范围内。另外，该方法还解决了强信号源未移除时对弱信号源的DOA估计问题。计算机仿真证明了算法理论是正确有效的。     

Maximum likelihood separation of constant modulus signals

In this correspondence, we derive a Newton scoring algorithm for the maximum likelihood separation and direction-of-arrival (DOA) estimation of constant modulus (CM) signals using a sensor array. We present a rapidly converging scheme for the joint estimation of the signals and their directions based on their CM property. We discuss initialization and show that the complexity is moderate     

Misadjustment and tracking analysis of the constant modulus array

The constant modulus (CM) array is a blind adaptive beamformer that can separate cochannel signals. A follow-on adaptive signal canceler may be used to perform direction finding of the source captured by the array. In this paper, we analyze the convergence and tracking properties of the CM array using a least-mean-square approximation. Expressions are derived for the misadjustment of the adaptive algorithms, and a tracking model is developed that accurately predicts the behavior of the system during fades. It is demonstrated that the adaptive canceler contributes more to the overall misadjustment than does the adaptive CM beamformer. Computer simulations are presented to illustrate the transient properties of the system and to verify the analytical results     

一种基于恒模算法的多用户盲波束形成新方法

提出了一种适用于多用户的盲波束形成新方法.该方法基于最小二乘恒模算法,当最小二乘恒模算法收敛于某一信号后,我们导出了算法的权向量与其它用户波达方向的关系,进而在不同用户的波达方向上形成初始增益,从而实现对所有用户的波束形成.文中给出了算法的并行和串行实现方案.     

DOA estimation of noncircular signals for coprime linear array via locally reduced-dimensional Capon

We investigate the issue of direction of arrival (DOA) estimation of noncircular signals for coprime linear array (CLA). The noncircular property enhances the degree of freedom and improves angle estimation performance, but it leads to a more complex angle ambiguity problem. To eliminate ambiguity, we theoretically prove that the actual DOAs of noncircular signals can be uniquely estimated by finding the coincide results from the two decomposed subarrays based on the coprimeness. We propose a locally reduced-dimensional (RD) Capon algorithm for DOA estimation of noncircular signals for CLA. The RD processing is used in the proposed algorithm to avoid two dimensional (2D) spectral peak search, and coprimeness is employed to avoid the global spectral peak search. The proposed algorithm requires one-dimensional locally spectral peak search, and it has very low computational complexity. Furthermore, the proposed algorithm needs no prior knowledge of the number of sources. We also derive the Crámer-Rao bound of DOA estimation of noncircular signals in CLA. Numerical simulation results demonstrate the effectiveness and superiority of the algorithm.     

A numerical pattern synthesis algorithm for arrays

A numerical technique for pattern synthesis in arrays is presented. For a given set of elements, the technique allows one to find a set of array coefficients that steer the main beam in a given direction and yield sidelobes meeting a specified criterion, if such a set of array coefficients exists. If the pattern specifications cannot be met with the given elements, the algorithm finds the best attainable pattern. The advantage of this technique is that it can be used with an arbitrary set of array elements. Different elements in the array can have different element patterns, and the array can have arbitrary nonuniform spacing between elements. The synthesis technique is based on adaptive array theory. The given array elements are assumed to be used as the elements of an adaptive array. The main beam is pointed in the proper direction by choosing the steering vector for that direction, and the sidelobes are controlled by introducing a large number of interfering signals at many angles throughout the sidelobe region. The algorithm iterates on the interference powers until a suitable pattern is obtained     

非圆信号阵列测向的研究概况

阵列测向的理论与技术已日臻完善，近期研究主要集中在算法实用化以及利用信号的特殊性质等方面。BPSK等信号具有非圆特性，MSK等信号经过一定预处理也具有非圆特性，利用信号非圆特性的阵列测向问题逐渐成为一个研究热点。文章介绍了非圆信号的定义与分类，综述了非圆信号测向的现状，包括各种非圆信号测向算法及其性能分析，以及非圆信号信源个数估计等其它问题，并对其发展方向进行了预测。     

一种基于余弦特性的快速DOA估计算法

为了满足实时测向处理系统的要求,需要降低测向算法的运算量。在众多测向算法中,其中基于圆阵的MUSIC类算法因为其测向频段宽,可估计二维波达方向,性能稳健,测向精度高,并且在各个方向上具有相同的测向性能而广泛应用于实际系统中。本文主要针对均匀圆阵的阵列流形,分析了均匀圆阵导向矢量的特点,提出了一种基于余弦特性的快速DOA估计算法。该算法利用余弦信号周期特性来构造统计量,通过数学推倒可以证明该统计量只与俯仰角有关,与方位角无关。通过这种分维处理的思想从而可以先估计出俯仰角,再通过所得俯仰角的空间谱切片把二维搜索转化为一维搜索,这样处理大大降低了算法运算量,提高了算法估计速度,特别适合阵元数目较多的应用场合。同时考虑到实际应用中在同一个窄带信道中干扰信号和期望信号同时存在,并且互相影响,特别是强信号对弱信号的影响。本文利用数字滤波器提取期望信号估计,减少了干扰信号对期望信号的影响,提高了估计精度。通过计算机仿真验证了该算法的有效性。      

相干宽带线性调频信号的波达方向估计新方法

提出了一种相干宽带线性调频(LFM)信号的波达方向(DOA)估计新方法。该方法利用LFM信号在分数阶Fourier域上的解线调特性,构造出新的解线调域阵列数据模型,然后结合传统的矩阵重构解相干以及MUSIC算法实现相干LFM信号的DOA估计。若同时存在多组相干LFM信号入射,则首先在不同的能量聚集域上将各信号组分离,然后逐一进行各组内相干信号的DOA估计。该方法充分地挖掘了观测信号所包含的时频信息,增加了可检测的DOA数目,提高了分辨性能和抗噪声性能。此外,该方法无冗余阵元与孔径损失,且适用于任意流型阵列。仿真结果显示,在DOA估计的均方根误差(RMSE)相同时,与传统方法相比,本方法可获得8dB左右的信噪比增益。     

阵列误差条件下MIMO 雷达测向敏感性分析*

测向敏感性分析对高性能阵列雷达系统设计和参数校正具有重要意义。针对MIMO 雷达系统误差 的测向敏感性问题,基于MUSIC 算法的一阶泰勒展开,研究了由MIMO 雷达收、发阵元幅度、相位和位置误差引起的 实际阵列与理想阵列间存在的流型误差对测向性能的影响;三种误差条件下分别定义和推导了任意阵型配置下MIMO 雷达测向敏感因子、方位估计RMSE 以及成功分辨误差门限的表达式。仿真实验验证了理论推导和分析方法的 有效性,相关结论可为MIMO 雷达系统分析与设计提供参考。     

双重不变ESPRIT

ESPRIT利用了阵列在一个方向上的平移不变性,从而可估计信号的一个方向。该文利用3个相互位移的子阵列所具有的双重不变性质,从而估计出了信号的两个方向,并且计算量较小,两个方向角自然配对,对子阵列的内部结构也没有要求,因此可以选择适当的子阵列结构以获得良好的估计效果。     

离散傅里叶变换及变参考阵元特征法在相干信号估计中的应用

本文讨论理想条件下均匀线阵(ULA)对相干入射信号的高分辨测向问题。在分析经典多重信号分类法(MUSIC)对相干信号测向失效原因的基础上提出新算法,它利用1）离散傅里叶变换(DFT)估计入射信号数目;2）变参考阵元重构入射信号功率矩阵估计入射信号方位角。通过与经典MUSIC算法比较验证了该算法对相干入射信号估计的可行性,并经过进一步分析得出如下结论:1）文中介绍的算法在不减少阵列有效口径前提下能够对高度相干信号进行高分辨测向;2）在低信噪比条件下能够精确估计入射信号方位角;3）随着阵列中阵元数目的增加,阵列分辨率逐渐提高。      

On blind beamforming for multiple non-Gaussian signals and theconstant-modulus algorithm

A new method for blindly separating multiple cochannel non-Gaussian signals received by a sensor array is presented. The method is based on a cumulant-based least-squares criterion that, for identically distributed negative-kurtosis signals, is proven to be identical to the “2-2” constant-modulus (CM) cost function commonly used by CM algorithms. A computationally simple algorithm is proposed to minimize the criterion. The algorithm performs well even when the number of samples is small, thus allowing its application in dynamic environments (e.g., moving emitters). For the special case of two signals only, the minimization is obtained analytically. Simulation results are included     

基于相控阵单通道接收机高分辨DOA估计的跟踪算法

本文提出一种基于相控阵单通道接收机系统的波达方向(DOA)高分辨跟踪算法。这种跟踪算法是用特定权实现单通道接收机高分辨测向的进一步完善。在信号源不相关的情况下,通过对权矢量的循环使用和功率信号的更新处理,实现对DOA时变信号源的跟踪。计算机模拟结果表明该方法具有良好的效果。     

基于时延分数阶相关函数时空处理的子空间测向算法

本文定义了一种时延分数阶相关函数和阵列输出信号的时延分数阶相关矩阵,在此基础上,提出了一种新的基于时延分数阶相关函数时空处理的了空间测向算法一时延分数阶相关MUSIC(TLFOC-MUSIC)算法。该算法在进行空域处理的同时,引入了时域处理,弥补了空域信息的不足,更充分地开发了空间信号中的有用信息,提高了算法的稳健性和测向的性能,对SαS噪声和干扰有较强的抑制作用。计算机仿真实验结果验证了该算法的有效性和稳健性。     

基于盲信号分离的同频多信号测向技术

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。常规的干涉仪算法只能够对非同频信号测向,当多个信号频谱重叠时,无法准确获得单个信号的相位,因此采用干涉仪算法不能够实现对同频多信号测向。提出了一种基于盲信号分离结果的干涉仪测向算法,可以解决同频多信号的测向,进一步提高了干涉仪算法的应用前景。     

宽带光控阵多波束超分辨测向算法

为了解决多信号并存时多波束系统的测向问题,针对宽带光控阵幅度加权和时延控制的多波束形成方式,提出了一种宽带波束域超分辨测向算法。建立了宽带光控阵多波束数据模型,首先将接收数据变换到频域,进行频谱分析,然后选择较大信号频率分量分别进行超分辨谱估计,最后将各频点估计结果融合得到最终的估计值。分析了波束参数的选取,包括波束数目、波束间隔以及幅度加权方式对算法性能的影响。算法能够分辨同时达到多波束系统的多个信号,仿真实验验证了算法的有效性和正确性。      

Multisource Self-Calibration for Sensor Arrays

Calibration of a sensor array is more involved if the antennas have direction dependent gains and multiple calibrator sources are simultaneously present. We study this case for a sensor array with arbitrary geometry but identical elements, i.e., elements with the same direction dependent gain pattern. A weighted alternating least squares (WALS) algorithm is derived that iteratively solves for the direction independent complex gains of the array elements, their noise powers and their gains in the direction of the calibrator sources. An extension of the problem is the case where the apparent calibrator source locations are unknown, e.g., due to refractive propagation paths. For this case, the WALS method is supplemented with weighted subspace fitting (WSF) direction finding techniques. Using Monte Carlo simulations we demonstrate that both methods are asymptotically statistically efficient and converge within two iterations even in cases of low SNR.      

平移嵌套阵列稀疏贝叶斯学习角度估计算法

针对阵元间互耦效应导致嵌套阵列测向性能下降的问题,该文提出两种不同的平移嵌套阵列结构,在保证产生虚拟阵列无孔的条件下,通过对原二级嵌套阵列阵元位置进行调整,形成平移嵌套阵列,提高了原二级嵌套阵列的稀疏性,降低了阵元间的互耦效应,扩展了原嵌套阵列的测向自由度。在空间辐射源数目未知条件下,建立了平移嵌套阵列稀疏贝叶斯学习(SBL)算法模型,对形成的虚拟阵列接收数据进行处理,获得角度估计,有效提高了原嵌套阵列测向算法的测向性能。仿真实验表明,平移嵌套阵列自由度高于原嵌套阵列,在低信噪比、小快拍数、存在互耦影响条件下,基于稀疏贝叶斯学习的平移嵌套阵列测向算法测向精度优于原嵌套阵列测向算法,并且提高了原嵌套阵列测向算法的角度分辨率。     

Direction finding algorithms based on high-order statistics

Two direction finding algorithms are presented for nonGaussian signals, which are based on the fourth-order cumulants of the data received by the array. The first algorithm is similar to MUSIC, while the second is asymptotically minimum variance in a certain sense. The first algorithm requires singular value decomposition of the cumulant matrix, while the second is based on nonlinear minimization of a certain cost function. The performance of the minimum variance algorithm can be assessed by analytical means, at least for the case of discrete probability distributions of the source signals and spatially uncorrelated Gaussian noise. The numerical experiments performed seem to confirm the insensitivity of these algorithms to the (Gaussian) noise parameters