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。     

Analysis of the spatial filtering approach to the decorrelation ofcoherent sources

The recently proposed spatial filtering, which is developed to effectively decorrelate coherent signals, is analyzed. It has been shown that any set of distinct preliminary estimates of directions of arrival (DOAs) can be used to obtain a full rank source covariance matrix. In addition, a particular signal enhancement approach is developed to minimize the effects of the sensor noise. Statistical analysis of the spatial filtering and its enhanced version are also studied using the Monte Carlo method     

相干信号源DOA估计的一种改进SVD算法

多重信号（MUSIC）算法是波达方向（DOA）估计中的一种标志性算法,在理想条件下具有良好的性能,但是当信号源相干时,算法的性能就会变得很差。为了使其在低信噪比、小角度条件下对相干信号源有着更好的分辨能力和稳定性,通过对解相干重要算法——矢量奇异值（SVD）算法的研究,并针对SVD算法在低信噪比、小角度条件下分辨能力的不足,提出了一种改进的矢量奇异值算法（NSVD）,即利用信号协方差矩阵的最大特征矢量,按一定规则构造出新矩阵,然后对矩阵进行修正,再利用奇异值分解算法估计出信号相关信息。最后通过大量的计算机仿真证明了算法的良好性能。     

On spatial smoothing for two-dimensional direction-of-arrivalestimation of coherent signals

We present an analysis of a spatial smoothing scheme extended for the estimation of two-dimensional (2-D) directions of arrival (DOAs) of coherent signals using a uniform rectangular array. The uniform rectangular array is divided into overlapping rectangular subarrays by the extended scheme, which is referred to as the 2-D spatial smoothing scheme. The analysis shows that when the extended preprocessing scheme is used in conjunction with the eigenstructure technique, the size of the subarrays should be at least (K+1)×(K+1), and the number of the subarrays must be no less than K×K in order to guarantee the “decorrelation” of κ coherent signals for all possible scenarios. The minimum size of the total uniform rectangular array is thus shown to be 2K×2K. Instead of using a uniform rectangular array, a minimal subarray structure incorporated with a minimal subarray grouping is also devised for resolving the 2-D DOAs of K coherent signals. The number of sensor elements of the minimal total array is then (K²+4K-2) instead of 4K²     

基于子阵近场零陷权的联合波达方向估计方法

针对海底长线阵在近场辐射声干扰及空间水平非均匀噪声下的远距离估计目标波达方向 (DoA)问题,该文提出一种基于长线阵分子阵近场零陷权的联合目标方位估计方法。该方法将长线阵分解为多个高重叠子阵,对各个子阵利用零陷抑制技术去除近场强干扰对目标探测的影响,再利用各子阵对远距离目标方位估计结果差异性小、非目标所在频率即噪声对应空间谱最大值随机的特点,空间频率方差加权综合各子阵的目标方位估计结果,从而抑制空间非均匀噪声,实现对远距离目标的探测。仿真结果表明,与长线阵常规波束形成、长线阵近场零陷常规波束形成、长线阵近场零陷传统多重信号分类方法相比,该文方法能够有效降低空间谱背景级60 dB以上,输出信噪比提高15 dB以上,具有较强的提高信噪比能力及较高的空间分辨力,因此具有较好的工程应用价值。     

Maximum likelihood direction-of-arrival estimation in unknown noise fields using sparse sensor arrays

We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise.     

虚拟空间平滑算法

根据MASK、BPSK和AM等信号的实值特性,利用阵列接收数据及其共轭信息,构造了虚拟子阵,提出了虚拟空间平滑算法,较好地解决了信号高度相关问题.虚拟子阵具有M个阵元,避免了阵列孔径的损失,使该算法最大可分辨的相干信号数目为M-1.仿真实验证明,与前后空间平滑算法相比,该算法提高了空间分辨率,适应小样本,在一定程度上可以降低了计算量.     

Efficient eigenspace-based array signal processing using multipleshift-invariant subarrays

This paper deals with the construction of eigensubspaces for adaptive array signal processing. An efficient technique for extracting the eigensubspaces spanned by the data vector received by an N-element adaptive array is presented. We first decompose the original array into several subarrays with multiple shift invariances and find the eigensubspaces corresponding to each of the subarrays. By solving a least-squares (LS) or total least-squares (TLS) problem, the signal and noise subspaces corresponding to the original array can be found from the eigensubspaces spanned by the subarray data vectors. Hence, there is no need to perform the eigenvalue decomposition of the N×N correlation matrix of the received data vector. The proposed technique significantly reduces the required computational complexity as compared to the conventional eigenspace-based (ESB) methods. In conjunction with the spatial smoothing scheme or a proposed cross-correlation method, this technique can also deal with the case of coherent signals. The effectiveness of the proposed technique is demonstrated by several computer simulations     

宽带独立信号和相干信号的DOA估计

当独立信号和相干信号共存时,传统四阶累积量方法无法估计出宽带相干信号的来波方向(DOA),针对这个问题提出了一种新方法。该方法首先通过离散傅里叶变换,将宽带阵列接收数据分解为若干个窄带信号,构造出各个窄带频率处的自相关矩阵,再通过MUSIC(Multiple Signal Classification)算法估计出各个窄带信号的DOA,将各个窄带信号的空间谱相加求平均,通过谱峰搜索得到宽带独立信号的DOA;然后分离出独立信号的信息,构造出一个只包含宽带相干信号信息的矩阵,最后通过稀疏重构的方法估计出相干信号的DOA。计算机仿真结果证明该算法的正确性和有效性。     

Maximum likelihood array processing for stochastic coherent sources

Maximum likelihood (ML) estimation in array signal processing for the stochastic noncoherent signal case is well documented in the literature. We focus on the equally relevant case of stochastic coherent signals. Explicit large-sample realizations are derived for the ML estimates of the noise power and the (singular) signal covariance matrix. The asymptotic properties of the estimates are examined, and some numerical examples are provided. In addition, we show the surprising fact that the ML estimates of the signal parameters obtained by ignoring the information that the sources are coherent coincide in large samples with the ML estimates obtained by exploiting the coherent source information. Thus, the ML signal parameter estimator derived for the noncoherent case (or its large-sample realizations) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound)     

基于协方差矩阵变换的相干源个数估计

正确地估计信号源个数是高分辨阵列信号处理的一个重要组成部分,本文提出了一种相干源个数估计的方法。这种方法是先采用空间平滑技术对信号源去相关,然后对阵列协方差进行酉变换,最后用基于协方差矩阵变换的信息论准则和盖氏半径(Gerschgorin Radii)两种方法估计信号源个数。计算机仿真结果证实了方法的可行性。     

近场散射体对阵列超分辨波达方向估计性能的影响和补偿方法

测定电波到达方向（DOA）是无线电定位系统的基本任务之一,先进的阵列超分辨测向技术获得广泛关注。在船载和机载等平台应用中,阵列天线由于受安装空间所限,其旁边存在散射体,这时阵列接收到的信号既包括远场目标信号又有近场散射体散射出多径信号。本文分析了这种近场散射信号对DOA估计性能的影响,即散射信号会在DOA谱上多形成一个干扰性质的谱峰,并且若散射信号来波方向靠近直达波来波方向时,会造成模糊现象。最后文章在多重信号分类算法（MUSIC）的基础上提出一种补偿方法,能够很好的抑制散射信号所造成的这种影响,实验仿真结果验证该算法的有效性。      

低信噪比下相干信号的DOA估计的白噪声滤除方法

在波达方向估计中,“相干”和“信噪比”一直引人关注。相干会使多重信号分类等算法失效,究其原因就是信源协方差矩阵的秩亏缺。低信噪比使阵列协方差矩阵的主次特征值区分困难,造成信号和噪声的子空间划分错误。针对相干,人们往往都是从“解相干”的角度出发,通过各种手段使信源的协方差矩阵能够满秩,但并未对秩亏缺特性加以利用。基于此,本文给出了一种在低信噪比下对相干源的波达方向估计的噪声消除方法,在仅有加性白噪声的环境下,利用相干信号协方差矩阵不能满秩的特点,通过求解方程组,用求的值代替估计的协方差矩阵的相关对角元素（即对角加载处理）,置换被噪声污染的对角元素,进而可以滤除掉白噪声的影响。仿真结果证实了方法的有效性。      

基于fMRI功能网络和贝叶斯矩阵分解的脑电源成像方法

脑电(EEG)是一种重要的脑功能成像技术,根据头皮记录的EEG信号重构皮层脑活动称为EEG源成像。然而脑源活动位置和尺寸的准确重构依然是一个挑战。为充分利用EEG和功能磁共振(fMRI)信号在时空分辨率上的互补信息,该文提出一个新的源成像方法——基于fMRI脑网络和时空约束的EEG源重构算法(FN-STCSI)。该方法在参数贝叶斯框架下,基于矩阵分解思想将源信号分解为若干时间基函数的线性组合。此外,为融合fMRI的高空间分辨率信息,FN-STCSI利用独立成分分析提取fMRI信号的功能网络,构建EEG源成像的空间协方差基,通过变分贝叶斯推断技术确定每个空间协方差基的相对贡献,实现EEG-fMRI融合。通过蒙特卡罗数值仿真和实验数据分析比较了FN-STCSI与现有算法在不同信噪比和不同先验条件下的性能,结果表明FN-STCSI能有效融合EEG-fMRI在时空上的互补信息,提高EEG弥散源成像的性能。     

On the coherent interference suppression using a spatiallysmoothing adaptive array

The behavior of a spatially smoothing adaptive array is examined. An expression for the weight vector is first derived. Using the array gain on the desired signal and the coherent interference is obtained. Then the expression for output signal-to-noise (SNR) is derived. It shows that the performance of the spatially smoothing array depends on the number of the subarrays, the angle separation, relative power and initial phase difference between the desired signal and the coherent interference. For good interference suppression it is found that the magnitude of the phase difference of the incident and interference signals must be greater than the beamwidths of both the subarray and the equivalent array. There is also a tradeoff between increasing the groups of subarrays and decreasing the number of elements in the subarrays. Computer simulation results are given that validate the analysis     

互质阵中基于降维求根的波达角估计算法

该文提出互质阵中基于降维求根的波达角(DOA)估计算法。互质阵包含两个稀疏均匀线性子阵,拥有互质的阵元间距和阵元数目。该算法基于子阵间的互协方差,利用较长子阵中的旋转不变性扩展较短子阵的虚拟孔径。然后通过矩阵分块构造噪声子空间,并将来自两个子阵的2维参数估计问题降维为1维求根问题,获得自动配对的2维模糊参数估计。最后由这2维模糊参数可恢复出两组参数,根据互质性从两组参数估计的交集中可以获得无模糊的高分辨率DOA估计。相比互质阵中的联合多重信号分类(MUSIC)算法和联合旋转不变技术(ESPRIT)算法,该算法无需特征分解,复杂度低,但可获得更精确的DOA估计,处理更多的信源,并且对色噪声有更强的鲁棒性。多个仿真结果均验证了所提算法的有效性。     

OFDM channel estimation in the presence of interference

We develop a frequency-domain channel estimation algorithm for single-user multiantenna orthogonal frequency division multiplexing (OFDM) wireless systems in the presence of synchronous interference. In this case, the synchronous interferer's signal on each OFDM subcarrier is correlated in space with a rank one spatial covariance matrix. In addition, the interferer's spatial covariance matrix is correlated in frequency based on the delay spread of the interferer's channel. To reduce the number of unknown parameters we develop a structured covariance model that accounts for the structure resulting from the synchronous interference. To further reduce the number of unknown parameters, we model the covariance matrix using a priori known set of frequency-dependent functions of joint (global) parameters. We estimate the interference covariance parameters using a residual method of moments (RMM) estimator and the channel parameters by maximum likelihood (ML) estimation. Since our RMM estimates are invariant to the mean, this approach yields simple noniterative estimates of the covariance parameters while having optimal statistical efficiency. Hence, our algorithm outperforms existing channel estimators that do not account for the interference, and at the same time requires smaller number of pilots than the MANOVA method and thus has smaller overhead. Numerical results illustrate the applicability of the proposed algorithm.     

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。     

雷达杂波视频信号实时仿真与系统实现

阐述了在雷达多目标仿真系统中杂波相干视频信号模型的建立过程和实时仿真系统的设计及实现。针对仿真过程中非正态随机序列的产生,提出用随机向量法和查表法进行零记忆非线性变换。随机向量法完成线性变换,查表法完成非线性变换,以产生具有指定概率分布和功率谱的非正态相干随机序列,复现与雷达信号具有相参性的杂波相干视频信号。仿真结果证明这些方法的正确性和实用性。     

Space-time fading channel estimation and symbol detection inunknown spatially correlated noise

We present maximum likelihood (ML) methods for space-time fading channel estimation with an antenna array in spatially correlated noise having unknown covariance; the results are applied to symbol detection. The received signal is modeled as a linear combination of multipath-delayed and Doppler-shifted copies of the transmitted waveform. We consider structured and unstructured array response models and derive the Cramer-Rao bound (CRB) for the unknown directions of arrival, time delays, and Doppler shifts. We also develop methods for spatial and temporal interference suppression. Finally, we propose coherent matched-filter and concentrated-likelihood receivers that account for the spatial noise covariance and analyze their performance