基于方向向量空间距离的干涉仪测向处理方法

本文提出了一种基于测向天线阵列阵列流形上方向向量的空间距离的干涉仪测向处理方法,利用该方法不用对长基线上的相位差测量数据进行匹配,而通过利用测得的一组有模糊的相位差数据构成的方向向量的估计值与阵列流形上的方向向量求空间距离,即可得到入射方位角的估计。与以往相比该方法不用进行数据匹配,并可避免匹配误差的传播,同时,该方法可以直接进行阵列流形误差的修正,因而具有处理简单、稳健、精度高等特点。     

基于Nyström的一种新颖的DOA估计算法

针对阵列信号处理领域中的超分辨子空间类算法需计算阵列输出的协方差、 协方差矩阵的特征分解及进行谱峰搜索得到波达方向(Direction of Arrival,DOA)估计,计算量较大则实际应用可能受限,提出了一种低计算复杂度的新颖的无需谱峰搜索的DOA估计算法.通过Nystr?m方法得到逼近的信号子空间,避免了直接对所...     

基于谱范数的矩阵CFAR检测器

基于信息几何的矩阵恒虚警率（Constant False Alarm Rate,CFAR）检测器为雷达目标检测问题提供了新的解决思路,其主要组成部分是均值矩阵的估计和检测统计量的计算,且检测性能与矩阵流形上的几何测度有紧密关系.现有的信息几何测度都是从Frobenius范数考虑,本文则基于谱范数考虑了矩阵流形上的几何测度和均值矩阵估计问题.将均值矩阵估计问题转化为矩阵流形上的优化问题,根据目标函数的性质得到了计算简便的近似均值矩阵.利用不同方法得到的均值矩阵,提出了几种新的基于谱范数的矩阵CFAR检测器.通过检测势分析和仿真实验验证了其检测性能优于现有的其他矩阵CFAR检测器,复杂度分析也表明了其计算复杂度低于现有的其他矩阵CFAR检测器,为海杂波背景下的雷达目标检测提供了新的有效技术手段.     

增益相位误差下基于部分校准嵌套阵列的DOA估计

现有的波达方向（Direction of Arrival,DOA）估计方法大多依赖于阵列导向矩阵的精确无偏条件,而实际工程中由于时钟偏移、阵元位置偏差的存在导致该条件往往难以满足.为匹配阵列实际接收条件,本文基于部分校准嵌套阵列,提出了一种增益相位误差下的DOA估计新方法.该方法首先利用连乘子函数和简单的代数运算完成初始增益相位误差估计,然后通过协方差矩阵向量化和稀疏表示理论构建具有连续自由度的稀疏表示向量模型,最后考虑有效样本的影响,在初始增益相位误差估计的基础上应用稀疏总体最小均方（Sparse total least squares,STLS）算法完成波达方向估计.本文所提方法不仅对阵列增益相位误差不敏感,而且可依靠嵌套阵列高自由度特性和STLS算法的抗扰动特性获得改进的分辨率和估计精度,计算机仿真结果验证了所提算法的有效性.     

稀疏表示框架下无需参数配对的二维到达角估计

现有二维到达角估计算法大多基于子空间理论及需要参数配对,针对这一问题,在稀疏表示理论框架下提出了一种参数自动配对的二维到达角估计新算法。该算法在L阵列下构建阵列互相关矩阵的稀疏表示模型,利用奇异值分解降低复杂度并基于群LASSO(Least Absolute Shrinkage and Selection Operator)获得方位角估计。在方位角估计的基础上,基于向量化操作构建稀疏空间谱匹配模型,然后利用LASSO 获得俯仰角估计。与参数配对ESPRIT 和改进的传播算子方法相比,所提算法不仅无需参数配对过程,而且可以提供改进的估计精度。计算机仿真结果验证了所提算法的有效性。     

一种数值稳健且低复杂度的信号子空间估计新方法

该文提出了一种数值稳健且低复杂度的信号子空间估计新方法。该方法通过多级维纳滤波器前向迭代构造观测数据协方差矩阵三对角化的转换矩阵,其列向量为信号子空间的一组正交基。与传统的相关相减结构结构相比,该文的多级维纳滤波器前向迭代通过Householder酉变换实现,显著增强了有限精度运算中信号子空间基向量的正交性,提高了数值稳健性。此外,基于Householder矩阵的酉性质和矩阵后向累积提出了一种转换矩阵的快速计算方法,降低了计算复杂度。计算机仿真结果验证了该方法的数值稳健性和计算效率。     

一种基于高阶累积量的近场源距离、频率和方位联合估计算法

本文提出了一种基于高阶累积量的近场源距离、方位及频率三维参数联合估计方法,该方法利用构造的累积量矩阵的特征分解获得信号子空间,三维参数估计分别由构造的矩阵的特征值直接估计得到,算法计算简单而无需任何搜索且适于任意高斯噪声环境,与现有的方法相比,本文算法由于无需利用阵列的中心对称结构,因而有效的避免了阵列的孔径损失.最后,计算机仿真实验验证了算法的性能.     

无模糊球面天线阵设计方法

基于测向天线阵列阵列流形上方向向量的空间距离,以球面天线阵为例,提出了一种用于无模糊阵列信号处理测向的有向天线元阵列设计方法。该方法不需要根据不同阵列的阵列流形上方向向量的空间距离的解析式进行复杂的推倒,而是从计算机模拟的角度对其进行探讨。该方法具有简单、灵活、稳健、可移植性强等特点。     

一种新的波达方向和多普勒频率估计算法

基于均匀圆形阵列,提出了一种同时估计空间非相干信号源方位角、仰角和多普勒频率的快速算法。该方法对均匀圆阵的输出信号进行模式空间转换,使得阵列流形具有类似于均匀线阵的形式,然后通过构造相应的数据矩阵得到传播算子的最小二乘（LS）估计,并由传播算子构造出一个特殊的低维矩阵,其特征值给出多普勒频率估计,特征向量舍有阵列流形的信息。结合模式空间阵列流形的性质,给出了一种DOA估计的总体最小二乘算法,在低信噪比条件下可提高测向精度。该方法不需要谱峰搜索和参数配对,具有运算量小的优点。计算机仿真验证了该方法的有效性。     

动平台下导航卫星多目标自跟踪方法研究

针对动平台下阵列天线对导航卫星自跟踪测角的问题,提出了一种采用导航信号解扩与阵列空间谱估计相结合的方法。通过信号解扩提高阵列接收的信噪比,通过MUSIC、ESPRIT和Mininorm等算法保证DOA估计的性能。仿真结果表明,3种算法的精度均能够达到0.1°,比较分析了不同算法的计算复杂度,提出了降低计算复杂度的方法。     

Givens rotation based least squares lattice and related algorithms

The author presents a general and systematic approach for deriving new LS (least squares) estimation algorithms that are based solely on Givens rotations. In particular, this approach is used to derive efficient Givens-rotation-based LS lattice algorithms-the Givens-lattice algorithms. By exploiting the relationship between the Givens algorithms and the recursive modified Gram-Schmidt algorithm, it is shown that the time and order update of any order-recursive LS estimation algorithm can be realized by employing only Givens rotations. Applying this general conclusion to LS estimation of time-series signals results in the Givens-lattice algorithms. Two Givens-lattice algorithms, one with square roots and the other without, are presented. It is shown that the Givens-lattice algorithms are computationally more efficient than the fast QR algorithm of Cioffi (1987). The derivation of other Givens rotation-based LS estimation algorithms and their systolic array implementations are discussed     

Direction of arrival estimation for more correlated sources than active sensors

In this paper, a new direction of arrival (DOA) estimation method for more correlated sources than active receiving antennas is proposed. The trick to solve this problem using only second-order statistics is to consider a periodic scanning of an underlying uniform array, where a single scanning period contains several time slots and in different time slots different sets of antennas are activated leading to a dynamic non-uniform array with possibly less active antennas than sources in each time slot. We collect the spatial correlation matrices of the active antenna arrays for all time slots and are able to present them as a linear function of the spatial correlation matrix of the underlying array. We provide a necessary and sufficient condition for this system of equations to be full column-rank, which allows for a least squares (LS) reconstruction of the spatial correlation matrix of the underlying array. Some practical greedy algorithms are presented to design dynamic arrays satisfying this condition. In a second step, we use the resulting spatial correlation matrix of the underlying array to estimate the DOAs of the possibly correlated sources by spatial smoothing and MUSIC. Alternatively, we can express this matrix as a linear function of the correlation matrix of the sources (incoming signals) at a grid of investigated angles, and solve this system of equations using either LS or sparsity-regularized LS (possibly assisted by additional constraints), depending on the grid resolution compared to the number of antennas of the underlying array.     

协作空分复用CSM系统中的信道估计算法研究

文章就当前MIMO应用过程中终端多天线受限制问题,阐述了单天线终端协作与基站组成虚拟MIMO多天线阵列——基于OFDMA的CSM协作空分复用系统结构。同时,提出对OFDMA系统中时频二维的资源进行分“块”独立进行信道估计,简化了OFDMA系统信道估计模型的复杂度。然后从应用的角度对比分析了基于OFDMA的CSM系统的信道估计算法LS和MMSE两种算法性能与优缺点。通过在PB3信道模型中仿真,得出适用于慢变信道的平滑滤波后的LS信道估计算法。     

一种利用旋转结构的分布式信号DOA估计方法

某些领域中由于多径影响信号往往具有分布特性,此时传统波达方向(DOA)参数估计算法往往失效.针对此类问题提出了一种无需搜索的分布式信号DOA估计算法,该方法利用已知信号有用信息逐一估计分布信号在相邻阵元间产生的旋转因子,利用DOA旋转不变结构估计分布目标DOA参数.与点目标ESPRIT方法相比该方法能够有效克服多径扩展影响,不敏感于多径分布的具体形式,并在一定程度上抑制相位误差影响.仿真实验证明了其有效性.     

Efficient direction and polarization estimation with a COLD array

This paper considers angle and polarization estimation by means of a cocentered orthogonal loop and dipole (COLD) array. We show that by using the COLD array, the performance of both angle and polarization estimation can be greatly improved, as compared to using a crossed dipole array. We present an asymptotically statistically efficient method of direction estimation (MODE) algorithm that can be used with the COLD array for both angle and polarization estimation of correlated (including coherent) or uncorrelated incident signals. Numerical examples are given to show the better estimation performance of the MODE algorithm than that of the multiple signal classification (MUSIC) and the noise subspace-fitting (NSF) algorithms     

Schur type algorithms for spatial LS estimation with highlypipelined architectures

A family of Schur-type spatial least-squares algorithms is presented for solving the spatial LS estimation problem, in which the correlation matrix is neither Toeplitz nor near-Toeplitz, by order recursion. Normalized spatial Levinson- and Schur-type algorithms are also derived. Highly pipelined architectures are designed to realize these recursions. The reflection coefficients are first computed using the spatial Schur type recursions. Then, the forward and backward filter parameters are calculated by the spatial Levinson-type recursions. A pyramid systolic array is demonstrated to calculate not only the filter parameters but also the LDU decomposition of the inverse cross-correlation matrix at every clock phase. This pyramid array can be mapped onto a two-dimensional systolic array which has a simpler structure. A square systolic array is developed to implement the Levinson- and Schur-type temporal recursive LS (RLS) algorithms. A highly concurrent architecture which exploits the parallelism of the spatial Schur-type recursions is illustrated to perform the LDU decomposition of the cross-correlation matrix     

智能天线中一种新的三维波束形成方法

提出了用分布于 x?y?z 三轴上的三个阵元估计扩频信号各条多径的来波方向(DOA)的方法,以及用由 x?y 均匀平面圆阵和 z 轴直线阵组成的阵列天线形成 3 维波束的方法。根据 CDMA 信号模型和多径信道模型,用 最大似然(ML)法分别估计出信号到达 3 个阵元的复信道响应,并利用复信道响应的幅角来估计各条多径信号 的 DOA;然后在已有最小均方误差(MMSE)法和最小二乘(LS)法的基础上生成带限制条件的扩展 MMSE(EMMSE)法形成 3 维波束。模拟表明,给出的 DOA 估计方法具有很高的精度,而 EMMSE 方法形成的波束主 瓣窄,旁瓣被抑制,有很高的输出信干噪比。     

2D DOA estimation for noncircular sources using L-shaped sparse array

We present an algorithm for two-dimensional (2D) direction-of-arrival (DOA) estimation of noncircular sources using an L-shaped sparse array. An L-shaped sparse array consisting of two co-prime arrays is firstly introduced. Then, the fourth-order-cumulants (FOCs) of received data are used to construct a FOC matrix (FOCM), by which we can get the estimations of elevation angles. With the estimated elevation angles, the azimuth angles can be estimated by a low-complexity signal separation algorithm. During the procedure used for estimating azimuth angles, no any eigenvalue decomposition (EVD), peak search and pair-matching procedure need to be implemented. Although the aperture is extended significantly, the computation complexity of proposed algorithm still is acceptable. Compared with some analogous algorithms, our approach shows more attractive estimation performance. A lot of simulation results prove the advantages of proposed DOA estimation technology.     

基于遗传算法的MIMO阵列位置误差自校正方法

天线阵元的位置误差会影响天线阵元所接收到信号的相位。基于特征值分解的高分辨率波达方向(DOA)估计算法对信号的相位误差非常敏感。针对多输入多输出(MIMO)阵列,本文基于遗传算法,利用自校正思想,构造一个对不同方向空间谱值进行加权求和的自适应权函数,结合MUSIC方法,构建个体适应度函数,实现了MIMO阵列阵元位置误差与DOA的联合在线估计。仿真结果表明该方法进行DOA估计的同时,还可以完成阵列位置误差的在线估计与校正,提高了系统参数估计的鲁棒性。     

On the least-squares performance of a novel efficient center estimation method for clustering-based MLSE equalization

Recently, a novel maximum-likelihood sequence estimation (MLSE) equalizer was reported that avoids the explicit estimation of the channel impulse response. Instead, it is based on the fact that the (noise-free) channel outputs, needed by the Viterbi algorithm, coincide with the points around which the received (noisy) samples are clustered and can thus be estimated directly with the aid of a supervised clustering method. Moreover, this is achieved in a computationally efficient manner that exploits the channel linearity and the symmetries underlying the transmitted signal constellation. The resulting computational savings over the conventional MLSE equalization scheme are significant even in the case of relatively short channels where MLSE equalization is practically applicable. It was demonstrated, via simulations, that the performance of this algorithm is close to that using a least-squares (LS) channel estimator, although its computational complexity is even lower than that of the least-mean squares (LMS)-trained MLSE equalizer. This paper investigates the relationship of the center estimation (CE) part of the proposed equalizer with the LS method. It is proved that, when using LS with the training sequence employed by CE, the two methods lead to the same solution. However, when LS is trained with random data, it outperforms CE, with the performance difference being proportional to the channel length. A modified CE method, called MCE, is thus developed, that attains the performance of LS with perfectly random data, while still being much simpler computationally than classical LS estimation. Through the results of this paper, CE is confirmed as a methodology that combines high performance, simplicity, and low computational cost, as required in a practical equalization task. An alternative, algebraic viewpoint on the CE method is also provided.