基于Toeplitz矩阵重构算法的宽带相干信号方位估计

根据天线阵列接收到的数据,提出了一种基于Toeplitz矩阵重构的宽带相干源方位估计算法.该算法先由Toeplitz 阵列接收到的数据得到包含波达方向信息的协方差矩阵,再对该协方差矩阵进行聚焦处理,得到同一频率的阵列协方差矩阵,最后由高分辨子空间处理方法得到宽带相干信号的波达方向估计.仿真实验证明了该方法的有效性.     

未知信源数目的DOA估计方法

针对信源数目未知情况下的DOA估计问题,该文提出了两种基于稀疏表示的DOA估计方法。一种是基于阵列协方差矩阵特征向量稀疏表示的DOA估计方法,首先证明了阵列协方差矩阵的最大特征向量是所有信号导向矢量的线性组合,然后利用阵列协方差矩阵的最大特征向量建立稀疏模型进行DOA估计;另一种是基于阵列协方差矩阵高阶幂稀疏表示的DOA估计方法,根据信号特征值大于噪声特征值的特性,通过对协方差矩阵的高阶幂逼近信号子空间,利用协方差矩阵的高阶幂的列向量建立DOA估计的稀疏模型进行DOA估计。理论分析和仿真实验验证,两种方法都不需要进行信号源数目的估计,具有较高的精度、较好的分辨力,对相干信号也具有优越的适应能力。     

基于空频域稀疏表示的宽频段DOA估计

该文提出一种基于空频域稀疏表示的宽频段波达方向(DOA)估计方法,解决稀疏表示方法在宽带接收机对窄带信号的频率和角度估计中的难题。用空间频率代替频率和方位角的 2 维组合构建过完备字典,字典的长度仅相当于窄带信号DOA估计的字典长度,却能覆盖整个无模糊频段,大大降低了稀疏分解的计算量。该方法首先在频域估计信号的准确频率,根据频域峰值的位置构建频域峰值协方差矩阵。对频域峰值协方差矩阵进行特征分解,利用主特征向量建立稀疏模型估计信号的DOA。算法在低信噪比下具有较高的估计精度,仿真实验和分析验证了该文方法的正确性和有效性。     

机翼可变阵列雷达的空时杂波建模与分析

针对可变飞行器阵列雷达空时自适应处理应用场景，本文设计了三维机翼可变阵列雷达模型，通过阵列结构参数的变化实现阵列横向长度变化、阵列纵向宽度变化、阵列曲面高度变化三种变形方式，利用阵元空间位置矢量与阵列结构变化参数集的关系建立阵列的空时杂波信号模型，推导了机翼变形阵列的空域导向矢量和杂波协方差矩阵，通过矩阵块嵌套的理论证明了它的非Toeplitz结构特性。通过对多普勒域和空频域之间相关性的分析，揭示了需要在四维空间中表征机翼可变阵列雷达空时杂波分布的本质机理。仿真结果表明，机翼可变阵列雷达的空时杂波具有非平稳特性，阵列变形可以有效改变杂波自由度和杂波相关性，这为基于三维机翼阵列结构优化的杂波抑制方法研究提供了理论依据。     

宽带波束域频率聚焦自适应波束形成算法

针对传统时延控制波束形成器无法适应宽带信号接收的问题,提出一种宽带波束域频率聚焦自适应波束形成算法。首先,通过时延控制方法在空间预设方位形成多个独立波束,利用傅里叶变换将多波束数据变换到频域,针对每个频点构造聚焦矩阵,通过聚焦变换将宽带信号对齐到同一参考频率;然后,估计子带聚焦后的协方差矩阵,并进行特征分解得到信号子空间;最后,利用基于特征子空间(ESB)的波束形成方法得到权矢量,实现自适应波束形成。仿真结果表明,该算法能够实现对宽带信号的自适应接收,具有良好的抗干扰能力和稳健性。     

一种新的互耦条件下宽带信号DOA估计算法

基于一致聚焦和Khatri-Rao子空间的思想,提出了一种新的互耦条件下宽带信号DOA 估计算法。首先,通过在阵列两端设置辅助阵元来补偿各个频率处的互耦效应;接着,对补偿后的协方差矩阵进行一致聚焦操作,并重排成一个高维矩阵;最后,通过搜索Khatri鄄Rao 子空间谱估计出宽带信号的DOA。算法适用于互耦未知的场合,对宽带信号频率谱是否平坦没有要求,且在互耦自由度较小时能够分辨多于阵元数的宽带信号。计算机仿真结果证明了算法的有效性。     

基于空间重采样的宽带DOA估计新算法及其性能分析

首先介绍了宽带阵列信号模型,然后给出了一种基于空间重采样的新的宽带信号相干子空间处理算法。该算法将均匀线阵看成是连续线阵的离散采样,在各个频率点调整阵列空间采样率,将各频率分量的方向向量聚焦到参考频率上,聚焦后得到单一频率点的数据协方差矩阵,再应用窄带MUSIC算法进行DOA估计,无需预估计波达方向,因此可以避免角度预估计对测向性能的影响。仿真实验表明新算法的性能要优于其他典型算法(TCT,RSS和TLS算法)。文中还给出了分辨率与阵元个数、信噪比之间关系。     

基于Bootstrap的宽带阵列信号个数检测

本文提出了一种基于Bootstrap的宽带阵列信号个数检测方法,该方法采用多重假设检验来估计信号的个数。通过对宽带信号的阵列采样输出分段并进行FFT变换,取其中一个频率点上的采样值构成阵列的采样协方差矩阵,得到假设检验的统计量,对选取的阵列采样输出使用Bootstrap方法得到检验阈值。该方法不需要对宽带阵列信号进行“聚焦”变换,且对高斯和非高斯信号均有很好的估计性能。计算机仿真试验证实了方法的有效性。     

宽带信号源个数估计频域bootstrap方法

针对宽带和非平稳信号,在未知信源类型及其它任何统计特性条件下,基于bootstrap方法,利用样本频域协方差矩阵的特征值特性,结合多假设检验过程,提出一种新的信源个数估计方法.理论分析和仿真实验表明该算法的有效性.     

一种估计宽带信号源数目的新方法

信号源数目估计是波达方向（DOA）估计的基础。基于噪声特征值的统计特性，文章在宽带信号条件下，基于各个频率点下的阵列采样协方差矩阵的噪声特征值在平面直角坐标系中几乎处于同一条直线上这一统计特性，提出了一种新的估计宽带信号源数目的方法，该方法简单且易于实现。最后通过计算机仿真验证了此方法的有效性和优越性。     

基于插值圓阵的宽带信号二维空间谱估计

在阵列信号处理中,确定信号的波达方向(DOA)需要估计信号的二维(2-D)空间谱。C。Usha Padmini等人(1994)已证明,圆阵用于估计宽带信号的DOA时具有许多好的特性。尤其是在基于圆阵的宽带信号子空间一维DOA估计中,即使不用延迟抽头也不会出现频率-方向模糊。在估计宽带信号的2-D空间谱时,我们发现用不带延迟抽头的圆阵会出现频率-仰角模糊。本文提出了一种用插值圆阵估计宽带信号2-D空间谱的新方法。在估计中,采用大孔径的圆阵(rmin/2)能获得更好的分辨性能和估计稳健性。     

2-D SPATIAL-SPECTRUM ESTIMATION FORWIDE-BAND SOURCES BASED ON INTERPOLATED CIRCULAR ARRAYS

In array signal processing, 2-D spatial-spectrum estimation is required to determine DOA of multiple signals. The circular array of sensors is found to possess several nice properties for DOA estimation of wide-band sources. C. U. Padmini, et al.(1994) had suggested that the frequency-direction ambiguity in azimuth estimation of wide-baud signals received by a uniform linear array (ULA) can be avoided by using a circular array, even without the use of any delay elements. In 2-D spatial-spectrum estimation for wide-band signals, the authors find that it is impossible to avoid the ambiguity in source frequency-elevation angle pairs using a circular array. In this paper, interpolated circular arrays are used to perform 2-D spatial-spectrum estimation for wide-band sources. In the estimation, a large aperture circular array (Υ>λmin/2) is found to possess superior resolution capability and robustness.     

高分辨率宽带相干信号波达方向的高效估计

本文给出了一种用于多源宽带相干信号波达方向(DOA)估计的快速算法。它是基于信号子空间技术,通过截取中心窄带频段数据并进行空间平滑来获得对方向的高效估计。文中最后通过计算机仿真,与CSM方法进行了比较,验证了本算法的快速、高分辨率等优良性能。     

利用空间时频分布实现宽带FM信号2-D到达角估计

提出了一种新的宽带FM信号二维到达角估计方法。本算法将L型天一阵两个臂的空间时频分布矩阵分别进行相干信号子空间平滑处理，并用子窨 方法实现方位和俯仰角估计，计算机仿真证实了算法的有效性。     

非均匀噪声环境下宽带近场源定位的两种改进方法

研究了宽带近场信号源基于最大似然方法和相关信号子空间方法在非均匀噪声下的被动定位算法，并进行了比较。这两种算法均可在传感器任意分布的情况下有效地进行信号源定位。最大似然法采用了迭代的方法来估计噪声的协方差矩。而信号子空间法给出了聚焦阵构造的新方法。仿真试验证明了方法的有效性和稳健性。     

Cyclostationarity-based coherent methods for wideband-signal source location

We propose a new approach for source localization of wideband signals impinging on an array of sensors. The proposed approach is signal-selective, i.e., it exploits the cyclostationarity property, exhibited by most communication signals, to discriminate signals of interest from noise and interfering ones. A coherent combination of the spatial contributions at different temporal frequencies is performed, and signal-subspace properties of the resulting focused matrix are exploited to obtain high-resolution source localization. Numerical results show that practical focusing techniques based on the new approach are superior to existing cyclic algorithms, also assuring good performances when the latter fail as, for example, when the signals of interest are fully correlated.     

Active bearing estimation from a mobile two-dimensional array of sensors

A new approach for active-sonar target detection and bearing estimation from a mobile two-dimensional array of sensors operating in a predominantly noisy environment is presented. Sensor-level adaptive noise cancellation featuring an unconventional method for reference-noise estimation is the key preprocessing step in the proposed approach. A signal-subspace algorithm resulting from two-stage optimisation based on a generalised eigendecomposition of the signal plus (residual) noise covariance matrix is employed to estimate the bearing of the detected target. Simulation results conclusively demonstrate that the proposed scheme is capable of performing target detection and the subsequent two-dimensional bearing estimation with a high degree of reliability at signal-to-noise power ratios as low as -70 and -40-dB, respectively.     

分布式目标参数估计的波束域处理方法

分布式目标参数估计的阵元域处理方法的运算量较大,将波束域处理方法用于分布式目标参数估计,降低分布式目标高分辨测向算法的运算量,提高输入信噪比,同时降低算法对系统误差的敏感性.在分布式目标源比较集中的情况下,利用较少的几个波束形成波束空间进行参数估计,能实现良好的估计性能.仿真结果验证了此方法的有效性.     

Wideband array processing using a two-sided correlationtransformation

A new method for broadband array processing is proposed. The method is based on unitary transformation of the signal subspaces. We apply a two-sided transformation on the correlation matrices of the array. It is shown that the two-sided correlation transformation (TCT) has a smaller subspace fitting error than the coherent signal-subspace method (CSM). It is also shown that unlike CSM, the TCT algorithm can generate unbiased estimates of the directions-of-arrival, regardless of the bandwidth of the signals. The capability of the TCT and CSM methods for resolving two closely spaced sources is compared. The resolution threshold for the new technique is much smaller than that for CSM     

Complex-valued ICA utilizing signal-subspace demixing for robust DOA estimation and blind signal separation

This paper deals with direction of arrival (DOA) estimation and blind signal separation (BSS) based on independent component analysis (ICA) with robust capabilities. An efficient demixing procedure of complex-valued ICA is presented here, which combines the signal-subspace demixing procedure exploiting individual signal-subspace projection and Newton’s iteration algorithm based on maximization of the approximate negentropy of non-Gaussian signal for array signal processing. It resolves the problems of order ambiguity and identifiability of traditional ICA for time-domain BSS. The proposed method could be directly applied to radar, sonar, radio surveillance, and communications systems for separating signals and estimating relative DOAs of signals. Several computer simulation examples for perturbations to the array manifold, unknown noise environments, and Rayleigh fading channel are provided to illustrate the effectiveness of the proposed method.