基于改进ESPRIT算法的波达方向估计

把经典的波达方向估计算法应用于均匀圆阵智能天线是一个重要的研究课题。通过预处理技术把均匀圆阵转换成虚拟均匀线阵,为了解决噪声造成信号子空间的扰动问题,提出了两种总体最小二乘ESPRIT（TLS—ESPRIT）;为把ESPRIT算法应用于均匀圆阵,引入了模式空间的ESPRIT算法。通过建立恰当的数学模型,对上述各算法的均匀线阵和均匀圆阵上的性能进行仿真和对比分析。仿真结果证明,两种改进的算法性能均好于基本的ESPRIT算法。     

基于极化敏感阵列均匀线阵的二维DOA估计

针对残缺电磁矢量传感器的极化敏感阵列多参数联合估计问题,该文提出一种基于正交偶极子的均匀线阵的2维波达方向(Direction-Of-Arrival, DOA)估计算法。首先,对极化敏感阵列的接收数据矢量的协方差矩阵进行特征分解,然后将信号子空间划分成4个子阵,根据旋转不变子空间(ESPRIT)算法分别求出其中1个子阵与其它3个子阵的相位差,再对不同子阵间的相位差进行配对,最后根据相位差求出信号的DOA估计和极化参数。由正交偶极子组成的均匀线阵使用极化MUSIC算法和传统ESPRIT算法无法进行2维DOA估计,该文提出的算法解决了这个问题,并且相较于极化MUISC算法降低了算法的复杂度。仿真结果验证了该文算法的有效性。     

TD-SCDMA中DOA估计的一种ESPRIT算法

智能天线是TD-SCDMA系统的关键技术之一,其核心为波达方向(Direction of Arrival,DOA)估计和波束形成算法。目前大多数DOA估计算法都是以均匀线阵为基础,而TD-SCDMA采用的是8阵元的圆阵列天线,因此,文中采用空间预处理技术,将圆阵虚拟到均匀线阵,再用ESPRIT算法进行DOA估计,最后通过仿真验证了该算法的可行性。     

基于时频子空间分解的宽带线性调频信号DOA估计

针对具有时变方向向量的宽带线性调频信号,该文建立了基于短时Wigner-Ville分布(WVD)的空间时频分布矩阵,通过对各个空间时频矩阵的特征分解获得对应的信号子空间和噪声子空间,给出了基于时频子空间投影实现多个时频点综合估计信号DOA的算法。利用空间时频分布的前后向平滑解决了具有相同时频特性信号的均匀线阵DOA估计问题。算法不需要聚汇和插值等复杂的矩阵变换,精度较高,计算简便.仿真实验显示该算法性能显著优越于基于矩阵插值的宽带调频信号DOA估计算法.     

一种基于最小冗余线阵的时空共轭循环谱相关ESPRIT算法

本文提出了一种基于最小冗余线阵的共轭循环谱相关ESPRIT算法。该算法引入了时空联合处理,构造了最小冗余线阵的伪数据矩阵,并基于此矩阵进行DOA估计。当采用M阵元最小冗余线阵时,该算法的等效阵列孔径为2N-1(NM)。理论和计算机仿真实验均表明:与基于均匀线阵的共轭循环ESPRIT算法相比,该算法具有良好的DOA估计性能,扩展了阵列孔径,抗噪能力强,分辨率高,能用较少的阵元估计更多的信号源方向。     

基于模式空间的均匀圆阵方位估计性能研究

在建立了均匀圆阵数学模型的基础上,将模式空间与经典高分辨方位估计算法——多重信号分类法（MUSIC）应用到均匀圆阵方位估计（DOA）中;详细推导了模式空间虚拟均匀线阵的形成过程,并通过计算机仿真给出了MUSIC算法在虚拟均匀线阵下的方位估计成功概率、估计方差、估计偏差等随信噪比变化时的变化情况。仿真结果表明,随着信噪比的增加,基于模式空间将均匀圆阵变成虚拟线阵,方位估计性能有了明显提高。     

一种信源个数与波达方向联合估计的新算法

针对多级维纳滤波器(MSWF)用于子空间估计时信号特征矢量泄漏到噪声子空间的问题,提出了一种新的信号子空间估计算法,该算法不需要训练信号和信源个数的先验知识.随后,给出了一种信源个数的后判断方法,最终完成信源个数及方向的同时估计.整个算法不需要协方差矩阵的计算和特征值的分解,具有较低的计算复杂度.在均匀线阵且信号互不相关情况下,改进后的算法用于波达方向估计时拥有与基于特征分解方法近似的性能.仿真结果验证了该方法的有效性.     

基于L型阵的ESPRIT算法在DOA估计中的应用

针对多信号同时测向问题,提出了一种基于L型阵的ESPRIT波达方向估计算法.利用阵元几何平移不变的特性,构造了包含多个入射信号特征向量的估计量,估计出入射信号的方位角和俯仰角.不需要二维谱峰搜索,能够在实数域中直接获得信号子空间,并且能够实现多个信号的同时估计,有效降低运算量.计算机仿真验证了算法的有效性.     

基于Toeplitz矩阵的波束空间新算法

为保留渡束空间算法优点,降低算法对阵列的要求,及对相干信号源有效,提出了基于Toeplitz矩阵的波束空间多重信号处理算法-BST-MUSIC.算法将任意阵列的波束域输出及均匀线阵的阵元输出综合处理得到Toeplitz矩阵,对其修正处理后采用MUSIC算法进行DOA估计.由推导过程知:算法能够增强或抑制指定空域内的信号;能使相干信源等效为独立信源;在任意阵列与均匀线阵阵元不重合情况下能够有效地抑制噪声;只需一个子阵为均匀线阵,降低了算法对阵列的要求.     

数据矩阵存在ESPRIT结构的一个定理及DM-ESPRIT算法

用传感阵列数据直接构成一定形式的数据矩阵仍具有ESPRIT[1]结构（EstimationofSignalparametersViaRotationalInvarianceTechniques），而不象过去用传感数据的空间协方差矩阵构成矩阵束．本文发现这一论断的存在定理并给出证明．基于这一定理产生了“数据矩阵的旋转不变性信号参数估计方法—DM－ESPRIT”算法．文章讨论了其优良的抗相关和抗噪声性能，并给出了计算机模拟结果．在性能模拟过程中还发现了DM－ESPRIT算法最优传感阵元数．     

Spatial signature estimation for uniform linear arrays with unknownreceiver gains and phases

The problem of spatial signature estimation using a uniform linear array (ULA) with unknown receiver gain and phase responses is studied. Sufficient conditions for identifying the spatial signatures are derived, and a closed-form ESPRIT-like estimator is proposed. The performance of the method is investigated by means of simulations and on experimental data collected with an antenna array in a suburban environment. The results show that the absence of receiver calibration is not critical for uplink signal waveform estimation using a plane wave model     

A Low-Complexity Algorithm for Coherent DOA Estimation in Monostatic MIMO Radar

This paper investigates the topic of direction of arrival (DOA) estimation for coherent sources in monostatic multi-input multi-output (MIMO) radar, and proposes a low-complexity algorithm for coherent DOA estimation. The direction vector of MIMO radar can be firstly mapped into a vector of virtual uniform linear array (ULA), and after that, a linear operator is constructed by partial cross-correlations from the received data of the virtual ULA. Finally, the DOAs can be obtained via roots finding method based on this linear operator. The DOAs can be estimated without any eigen-decomposition, nor evaluating all correlations of the received data. The proposed algorithm has much lower complexity as well as much better angle estimation performance than conventional forward backward spatial smoothing (FBSS)-propagator method (FBSS-PM), FBSS- estimation method of signal parameters via rotational invariance techniques (FBSS-ESPRIT), FBSS- root multiple signal classification (FBSS-Root MUSIC), and ESPRIT-like algorithm. Simulations present the effectiveness and improvement of our approach.     

Improved coherent DOA estimation algorithm for uniform linear arrays

An improved algorithm on coherent direction-of-arrival (DOA) estimation is presented in this article, with the objective to overcome the unsatisfactory performances of estimation of signal parameter via rotational invariance techniques (ESPRIT)-like algorithms (Han and Zhang, IEEE Antennas and Wireless Propagation Letters 2005;4:443–446). On the basis of trilinear model by reconstructing a series of Toeplitz matrix from the co-variance matrix of array output, our proposed algorithm is to resolve the DOAs of coherent signals, which not only has much better DOA estimation performance than algorithms of ESPRIT-like and multi-invariance ESPRIT but also identifies more DOAs than ESPRIT-like algorithm. Simulation results demonstrate its validity.     

基于空间特征的多用户循环累量域波达方向估计算法

本文提出了一种基于空间特征的多用户相干源ＤＯＡ估计算法。该算法构造了循环累量域窨特征矩阵,通过对空间特征矩阵的特征分解估计多用户信号的空间特征,并利用前后向平滑空间特征协方差矩我用户信号的ＤＯＡ。理论分析表明：该算法能估计共信道多用户相干信号的ＤＯＡ,并自动分组。它的另一个特点是具有ＤＯＡ估计的选择性。     

MODE SPACE SMOOTHING ALGORITHM TO ESTIMATE DOA OF COHERENT SIGNALS IMPINGING ON UNIFORM CIRCULAR ARRAY

A novel method to estimate DOA of coherent signals impinging on a uniform circular array( UCA) is presented in this paper. A virtual uniform linear array (VULA) is first derived by using spatial DFT technique, transforming the UCA from element space to phase mode space to obtain the properties of ordinary ULA, and then the well known spatial smoothing technique is applied to the VULA so that the lost rank of covariance matrix due to signal coherence can be retrieved. This method makes it feasible to use the simple MUSIC algorithm to estimate DOA of coherent signals impinging on a UCA without heavy computation burden. Simulation results strongly verify the effectiveness of the algorithm.     

Space-time channel estimation and performance analysis for wireless MIMO-OFDM systems with spatial correlation

This paper treats channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with correlation at the receive antenna array. A two-step channel estimation algorithm is proposed. Firstly, the iterative quadrature maximum likelihood based time delay and spatial signature estimation is presented by utilizing special training signals with a cyclic structure. The receive spatial correlation matrix of the vector valued channel impulse response is formulated as a function of the spatial signature, the time delay, and the pulse shaping filter. The joint spatio-temporal (JST) filtering based minimum mean squared error channel estimator is derived by virtue of the spatial correlation. In addition, the effect of channel estimation errors on the bit error probability performance of the space-time block coded OFDM system over correlated MIMO channels is derived. The Cramer-Rao lower bound on the time delay estimate is provided for a benchmark of the performance comparison. The performance of proposed algorithms is illustrated based on analysis and computer simulations. The JST channel estimator achieves significant gains in the mean squared error compared to the temporal filtering. It also enables remarkable savings in the pilot symbol power level.     

基于非圆信号的实值ESPRIT算法

在信号源为非圆信号的情况下,该文提出了一种实值ESPRIT波达方向(DOA)估计方法。根据非圆信号为实值信号的特点,利用欧拉公式将接收数据转化为正弦和余弦数据,并将其加以拼接,从而虚拟地将阵元个数加倍,然后在此基础上构造一个旋转不变结构来估计波达方向,新算法可以处理的信号个数是传统ESPRIT算法的两倍。由于在实值基础上进行特征值分解,所以该文提出的算法可以有效地将运算量减少到相同维数复值运算量的1/4。仿真实验表明新算法不仅估计精度高而且能够处理的信号个数可大于阵元个数。     

平衰落信道中的空间特征估计算法及其在空分多址中的应用

本语文提出了一种基于累量的多用户空间特征估计算法,并将该算法用于智能天线中空分多址的实现,该算法构造螺量域空间特征矩阵,通过对空间特征矩阵的特征分散得到各用户信号嚓间特征估计,基于空间特征的估计,设计了用于上行多波束成形的空间滤波器组,实现了上行多用户信号的分离,仿真实验表明,空间滤波器的响应可用于相干源的波达方向估计,为实现频分双工系统的下行选择性发送提供了依据。     

Double Codebook Beamforming Technique in Massive MIMO

In order to adapt double codebook beamforming technique in massive MIMO system, double codebook estimation algorithms based on the extended double codebook are proposed in this paper, such as one-dimensional (1D) estimation inherited MIMO, independent two-dimensional (2D) estimation and ESPRIT-like estimation. 1D estimation traverses the candidates based on the cross combination of two directions. Independent 2D estimation traverses the candidates based on two directions separately.  ESPRIT-like estimation is based on rotational invariance principle of antenna array, which imposes significantly lower computational complexity than the former two algorithms. Advanced, its complexity does not increase with the number of candidates. Finally, the complexity and system performance with three algorithms are compared and analyzed. The results show that 1D estimation gains the best performance, but it is too complex and difficult for practical implementation. ESPRIT-like estimation is more practical due to its reduced complexity and comparable performance with the independent 2D estimation algorithm, especially with small number of candidates.     

一种基于四阶累积量的空间特征盲估计方法

该文以均匀直线阵为基础,提出了一种基于阵列输出四阶累积量的信号空间特征盲估计方法,并将其用于相干多径环境下的多用户信号空间特征的估计。作者利用四阶累积量构造了一个空间特征估计矩阵,并证明了对该矩阵作特征分解可以得到各用户的空间特征。该盲算法不依赖于信号具体特征,适用于任意加性高斯(白或有色)噪声。理论分析和仿真结果说明了算法的有效性和稳健性。