ISAR雷达ESPRIT成像算法的改进

应用ESPRIT算法能够很好地提高逆合成孔径雷达成像的分辨率，但是传统的ESPRIT算法运算量大，运算速度慢。本文把对ESPRIT算法的一些最新成果即ESPRIT的改进引入ISAR成像过程，经过试验证明它能有效改善雷达成像的速度。     

一种改进的二维虚拟ESPRIT算法-虚拟波达方向矩阵法

二维虚拟ESPRIT算法对子阵列内部结构要求低,但在低信噪比环境下,其估计误差较大。提出一种改进的二维虚拟ESPRIT算法,该算法具有二维虚拟ESPRIT算法的优点,但在低信噪比条件下他的估计误差更小,算法运算量更低。计算机仿真实验表明了改进的二维虚拟ESPRIT算法的有效性。     

目标个数未知时双基地MIMO雷达多目标角度跟踪算法研究

针对目标个数未知时双基地MIMO雷达角度跟踪问题,该文提出一种基于改进自适应非对称联合对角化(AAJD)的目标个数与角度联合跟踪算法。AAJD算法中无法得到特征值变量,因此改进AAJD算法引入主成分顺序估计思想,循环求出特征值,然后运用改进信息论准则估计出目标个数。其次提出目标个数防抖动算法,提高了稳健性。最后改进了ESPRIT算法,完成了目标参数的自动配对和关联。仿真结果表明改进AAJD算法能够成功跟踪目标个数和角度,验证了理论分析的有效性。     

基于最优子阵划分ESPRIT的任意线阵高精度DOA估计算法

为了提高任意阵列的波达方向（direction of arrival, DOA）估计性能,从对子阵阵元选取进行优化的角度出发,提出了基于最优子阵划分旋转不变信号参数估计技术(estimation of signal parameters via rotational invariance techniques, ESPRIT)的任意线阵高精度DOA估计算法. 该算法首先利用虚拟插值阵列ESPRIT（virtual interpolated array ESPRIT, VIA-ESPRIT）得到精度较低的DOA粗估计. 其次以DOA粗估计为参考对任意阵列进行相位补偿,使其具备旋转不变性. 然后根据ESPRIT算法原理对构建旋转不变方程的子阵划分进行优化,并通过优化后子阵间的旋转不变性得到高精度的DOA估计. 此外,本文还分析了子阵划分对算法估计性能的影响,给出了子阵最优选取的近似计算方法. 计算机仿真结果验证了所提算法的有效性,并表明其性能逼近克拉美·罗界（Cramer-Rao bound, CRB）.     

基于ESPRIT宽带信号测向技术研究

研究了基于ESPRIT宽带信号测向技术,采用信号子空间变换方法分别对2个平移矩阵聚焦处理,并利用窄带ESPRIT测向方法计算出来波方向,提出利用ESPRIT与聚焦处理相结合测向的方法,解决了ESPRIT宽带信号测向精度差问题。通过聚焦处理有效抑制噪声,提高信噪比,从而获得高精度、高分辨率的渐进无偏估计信号方向。计算机仿真验证了算法的有效性。     

酉ESPRIT超分辨ISAR成像方法

针对ESPRIT超分辨成像方法没有利用复数共轭数据且难以确定散射点数目的不足,提出了采用酉ESPRIT实现ISAR超分辨成像的新方法.该方法利用改进的盖式圆盘方法确定散射中心的数目,克服了ESPRIT方法中无法确定散射中心数目的缺点.通过合成复观测数据及其共轭,提高了ESPRIT超分辨成像的分辨率.构造了中心复共轭对称矩阵,有效降低了计算量.利用仿真数据和实测数据对该方法进行了验证,结果表明该方法不但具有更优的抗噪性能和分辨率,也具有更高的运算效率.     

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。     

一种改进的ESPRIT测向算法

ESPRIT测向算法需要预先知道来波的数目。本文对ESPRIT算法进行了改进。改进后的算法把对空间来波数目的估计和空间来波方向的估计有效地结合起来，从整体上减少了运算量，而没有降低测向性能。最后进行了计算机仿真。仿真结果表明，该算法切实可行，具有工程应用价值。     

一种基于ESPRIT的改进算法

在目标波达方向估计的众多算法中,ESPRIT算法是一种运算速度较快、估值精度较高的常用算法.在该算法中,利用两个相同平移阵列接收信号的协方差矩阵进行广义特征值分解,进而求出目标波达方向的高分辨率估计,但是由于需要对协方差矩阵进行广义特征值分解运算,大大增加了计算的复杂度,因而不利于在工程应用中实现.本文提出了一种基于ESPRIT的改进算法,不需要对来波信号的协方差矩阵进行广义特征值分解,就可以实现对目标波达方向的高分辨估计,理论分析和仿真结果证明了该改进算法的有效性和可行性.     

色噪声背景下的二维谐波频率估计

针对色噪声背景下的二维谐波频率估计问题,本文提出了拓广的二维ESPRIT算法.该算法对二维MA模型的噪声具有比较好的抑制能力.同时,对于二维频率配对问题,本文给出了一种更为简便的方法.仿真实验验证了算法的正确性.     

Unitary ESPRIT: how to obtain increased estimation accuracy with areduced computational burden

ESPRIT is a high-resolution signal parameter estimation technique based on the translational invariance structure of a sensor array. Previous ESPRIT algorithms do not use the fact that the operator representing the phase delays between the two subarrays is unitary. The authors present a simple and efficient method to constrain the estimated phase factors to the unit circle, if centro-symmetric array configurations are used. Unitary ESPRIT, the resulting closed-form algorithm, has an ESPRIT-like structure except for the fact that it is formulated in terms of real-valued computations throughout. Since the dimension of the matrices is not increased, this completely real-valued algorithm achieves a substantial reduction of the computational complexity. Furthermore, Unitary ESPRIT incorporates forward-backward averaging, leading to an improved performance compared to the standard ESPRIT algorithm, especially for correlated source signals. Like standard ESPRIT, Unitary ESPRIT offers an inexpensive possibility to reconstruct the impinging wavefronts (signal copy). These signal estimates are more accurate, since Unitary ESPRIT improves the underlying signal subspace estimates. Simulations confirm that, even for uncorrelated signals, the standard ESPRIT algorithm needs twice the number of snapshots to achieve a precision comparable to that of Unitary ESPRIT. Thus, Unitary ESPRIT provides increased estimation accuracy with a reduced computational burden     

Blind Carrier Frequency Offset Estimation for OFDM System with Multiple Antennas using Multiple-invariance Properties

In this paper, we address the problem of carrier frequency offset (CFO) estimation for Orthogonal Frequency Division Multiplexing (OFDM) communications systems with multiple antennas. We reconstruct the received signal to form data model with multi-invariance property, and subsequently derive a multiple-invariance ESPRIT algorithm for CFO estimation. This algorithm has improved CFO estimation compared to ESPRIT method and maximum likelihood method. Simulation results illustrate performance of this algorithm.     

估计相干与非相干信源的ESPRIT新方法

在目前信号波达方向（DOA,Direction-of-Arrival）估计中,ESPRIT算法是一种速度快、精度高的常用算法,但对于低信噪比下混合信号（同时含有相干与非相干信号）,ESPRIT算法难以估计出它们的DOA。提出了一种新的同时估计相干与非相干信源的ESPRIT方法,新方法充分利用数据协方差矩阵的自相关信息和互相关信息来重构含有信号方位信息的矩阵,再从它的特征值中解得信号的到达角。新方法解决了常规ESPRIT算法不能解相干,对信噪比要求高等问题,且与同类算法相比较,分辨能力和估计精度明显提高。仿真实验证明了该方法在混合信号估计中的优越性和可靠性。     

Conjugate ESPRIT (C-SPRIT)

In this paper, we present an algorithm to estimate the direction of the arrival angles (DOAs) from noncoherent one-dimensional (1-D) signal sources such as binary phase shift keying (BPSK) and M-ary amplitude shift keying (MASK). The proposed algorithm can provide a more precise DOA estimation and can detect more signals than well-known classical subspace-methods MUSIC and ESPRIT for the 1-D signals. The complexity is the same as that of ESPRIT since the proposed algorithm uses the same array geometry and subarray processing that ESPRIT does. The main differences between the proposed algorithm and the ESPRIT algorithm are as follows: 1) the number of overlapping array elements between two subarrays is equal to M in the proposed algorithm, while in ESPRIT the maximum number of overlapping elements is M-1, where M denotes the total number of array elements, and 2) the proposed algorithm employs the conjugate of rotation matrix (CRM) /spl Phi//sup */ while ESPRIT uses /spl Phi/ with no conjugate for the second subarray geometry.     

基于数据共轭重排的修正ESPRIT信号DOA估计算法

本文介绍了将接收数据共轭重排的再利用,构造相关矩阵的修正ESPRIT算法.理论分析和仿真实验表明,该算法同经典的ESPRIT算法相比,在快拍次数有限时,可明显改善信号DOA估计的性能,且其计算量二者基本相当.     

信号DOA和极化参数的自适应估计

运用ESPRIT算法进行信号参数的估计,关键是对信号子空间的估计。在实际运用中,接收到的信号的参数往往是随时间变化的,因此要得到信号参数的实时估计值就需要根据阵列的接收数据对信号子空间进行更新。分析了如何运用ESPRIT算法得到信号的DOA和极化参数,并在矩阵扰动理论的基础上,利用矩阵特征分解一阶修正方法更新特征值和特征向量,从而使得ESPRIT算法能够自适应地估计信号DOA和极化参数。最后通过Matlab仿真验证了该方法的有效性。     

自适应频率和二维波达方向角跟踪估计算法

运用特征子空间分析方法的关键问题在于信号或噪声子空间的估计,在实际中有些信号的统计特性通常是随时间变化的,这时需要随时根据新的阵列接收数据对信号或噪声子空间进行更新,以得到参数的实时估计值,在该文中建立了多维信号参量联合估计的3D Unitary ESPRIT算法,然后提出了基于球面平均 ULV分解的子空间跟踪算法,将子空间跟踪算法与多维信号多量联合估计算法相结合,得到多维时变信号参数的跟踪估计算法,仿真计算结果验证了该算法的有效性。     

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.     

Reduced-Dimensional ESPRIT for Direction Finding in Monostatic MIMO Radar with Double Parallel Uniform Linear Arrays

In this paper, the issue of two-dimensional direction of arrival estimation in monostatic multiple-input–multiple-output (MIMO) radar with double parallel uniform linear arrays is studied, and an algorithm based on estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed. Through a series of reduced-dimensional transformations, the proposed algorithm has very low complexity due to the low dimension. Meanwhile, the estimation performance of the proposed algorithm is slightly improved compared to the conventional ESPRIT, especially in low signal-to-noise ratio. Furthermore, the algorithm can estimate azimuth and elevation angles without additional pair matching in monostatic MIMO radar. Error analysis of the angle estimation and Cramér–Rao bound are derived. Simulation results verify the usefulness of our algorithm.