OFDM系统载频偏移的ESPRIT方法估计

分析了OFDM系统信号模型与经典ESPRIT方法所要求的观测向量之间的相合之处，给出了利用经典ESPRIT算法估计OFDM系统载频偏移的方法。然后尝试使用了一种经典ESPRIT算法（TLS—ESPVaT）估计载波频率偏移，仿真结果表明使用该方法比类ESPRIT算法具有更高的估计精度，证明了使用经典ESPRIT方法估计栽波频率偏移的可行性与有效性。     

基于ESPRIT算法的子载波交织型OFDMA上行链路频偏估计

频率同步问题对于OFDMA系统来说至关重要。本文针对子载波交织分配的OFDMA系统上行链路，利用其接收信号周期性的结构特征，提出一种基于ESPRIT算法的多用户频偏估计算法，通过子空间旋转不变的特点估计各用户的载波频偏。仿真表明，利用该算法进行频偏估计具有良好的准确性。     

基于ML和ESPRIT方法的OFDMA上行链路频偏估计算法

由于载波频偏会给正交频分多址接入OFDMA系统带来子载波之间干扰,从而造成多用户之间的干扰,导致系统性能下降.该文提出一种基于ESPRIT和ML方法联合估计的频偏估计算法.该算法首先使用ESPRIT方法估计出多个可能的频偏构成的子集,然后使用最大似然估计方法在这个有限子集合中搜索出估计的频偏.该算法解决了使用似然估计进行多维搜索的问题,大大降低了算法的复杂度,同时解决了多个频偏估计的问题.     

频谱估计算法在炮口初速测量雷达工程的实现

雷达信号频率参数的测量结果决定了测速雷达的精度。介绍了两种频率估计方法,并应用FFT、MUSIC和ESPRIT算法对高斯白噪声下的多普勒速度进行了估计。通过结果分析,比较了在不同条件下,几种算法的估计精度。结果表明,在信号一定信噪比条件下,采用MUSIC、ESPRIT算法能够实现高精度频率估计。     

基于CP-ESPRIT的MIMO-OFDM频偏估计算法

针对已有MIMO-OFDM盲同步算法需要较多接收数据的缺点,提出了一种基于循环前缀和ESPRIT思想的MIMO-OFDM频偏估计算法——CP-ESPRIT算法。算法利用接收信号中未被IBI污染的循环前缀和OFDM符号中相应的部分构造接收信号矩阵,然后利用ESPRIT方法求得频偏估计值。该算法只要一个符号就能估计频偏,且频偏估计范围为一个子载波宽度,可用于载波频偏精同步。仿真验证了算法的性能。     

一种有效的OFDM载波频率偏移的盲估计

导出了具有循环前缀（CP）和虚子载波的正交频分复用（OFDM）通信系统信号的薮学模型。提出了基于PM—ESPRIT算法的0FDM系统载波频率偏移（CFO）偏移不变性的估计算法。经过粗略估计,该算法可以同时获得频率偏移和包含信道信息的矩阵,它们均有利于接收信号的偏移补偿和解调。仿真结果说明了该方法是有效的。     

基于ESPRIT算法频率估计的研究

本文提出了一种新的研究频率估计的算法-ESPRIT,该算法利用的是信号子空间的旋转不变性。具体讲述了ESPRIT算法在频率估计上的应用,先描述这种算法在理论上的实现,接着用仿真来证实这种算法的可行性和优势。     

MPSK信号载频快速估计算法

针对MPSK信号载波频率估计问题,文中将谱线检测理论与非线性变换思想相结合,提出了一种载波频率快速估计算法.详细介绍了算法原理,并给出了算法性能仿真,结果表明,文中提出的算法可以达到快速而准确地估计MPSK信号载波频率的目的.     

调频遥测信号载波频率的精确估计

传统调频遥测信号载波频率估计算法对输入信号降采样后直接进行快速傅里叶变换，实现方法虽然简单，但测量精度较差，无法适应高动态、低信噪比等复杂场景。为此，提出了一种调频遥测信号载波频率的精确估计算法。两并联补偿支路先分别采用正、负调频频率对输入信号进行频率预先补偿，低通滤波后完成降采样处理，削弱调频频率的频谱影响；频率搜索状态对采样数据进行载波多普勒变化率的频率补偿，经过快速傅里叶变换、非相干积分和频谱重心搜索完成频率解算，提高载波频率的检测性能。试验与分析表明，所提算法在高动态、低信噪比等复杂场景下可显著提高调频遥测信号载波频率的估测性能。     

一种适用于QAM信号的数字载波估计算法

由于发射和接收机振荡器的不稳定或/和信道的多普勒效应,使载波同步与补偿成为QAM信号接收的关键环节。针对非协作通信中各类载波同步算法只能校正较小的频率偏移的局限,根据QAM信号的基本特点,利用传输符号的统计独立特性,提出了一种新的数字载波频率估计方法。该算法采用开环方式,不需要导频数据,可以估计较大的频率偏移;没有反馈环路,复杂度低,实现起来比较简单。同时,该算法作适当改进,可以直接用于载波的估计。计算机仿真结果表明:该算法可以较好地对各种QAM信号中存在的较大频率偏移进行估计并做补偿;适当改进后可以直接用于估计载波频率,估计效果良好有效。     

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.     

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。     

Modified ESPRIT (M-ESPRIT) algorithm for time delay estimation in both any noise and any radar pulse context by a GPR radar

This paper presents M-ESPRIT, a modified version of the ESPRIT algorithm, for the purpose of time delay estimation of backscattered radar signals. The proposed algorithm takes both the transmitted pulse shape and any noise into account. It can process raw data from experimental device without the preprocessing which would be required with the conventional ESPRIT algorithm.     

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     

一种改进的ESPRIT方法

提出一种改进型的ESPRIT算法,对传统ESPRIT算法作以改进,提高了ESPRIT方法的性能,在小信噪比、多信号源的情况下提高了算法的测角精度.通过计算机仿真对改进算法的效果作了验证,验证了算法的有效性.     

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.     

URV ESPRIT for tracking time-varying signals

ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Unfortunately, its computational burden makes it unsuitable for real time processing of signals with time-varying directions of arrival. The authors develop a new implementation of ESPRIT that has potential for real time processing. It is based on a rank-revealing URV decomposition, rather than the eigendecomposition or singular value decomposition used in previous ESPRIT algorithms. The authors demonstrate its performance on simulated data representing both constant and time-varying signals. They find that the URV-based ESPRIT algorithm is effective for estimating time-varying directions-of-arrival at considerable computational savings over the SVD-based algorithm     

共轭增强ESPRIT算法

提出了共轭增强ESPRIT（CA-ESPRIT）算法，利用阵列输出的延迟相关函数及其共轭形成伪阵列输出，从而得到伪协方差矩阵，对其进行特征分解，用ESPRIT算法得到波迭方向。仿真实验表明，新算法可对多于阵元数的信号进行测向，其测角精度和分辨能力优于ESPRIT算法，运行时间小于有相同阵列扩展能力的MUSIC—like算法。     

Multiple invariance ESPRIT

A subspace-fitting formulation of the ESPRIT problem is presented that provides a framework for extending the algorithm to exploit arrays with multiple invariances. In particular, a multiple invariance (MI) ESPRIT algorithm is developed and the asymptotic distribution of the estimates is obtained. Simulations are conducted to verify the analysis and to compare the performance of MI ESPRIT with that of several other approaches. The excellent quality of the MI ESPRIT estimates is explained by recent results which state that, under certain conditions, subspace-fitting methods of this type are asymptotically efficient     

Multidimensional multirate DOA estimation in beamspace

This paper presents a beamspace version of ESPRIT for uniform rectangular arrays that supports closed-form 2-D angle estimation, automatically couples the two components of the source directions, and works with any front end beamformer. The proposed algorithm is based on the observation that beamspace noise eigenvectors can be transformed to vectors that are bandpass and have spectral nulls at the inband source locations. This facilitates multirate processing (involving modulation to baseband, filtering, and decimation) and yields a space with dimensionality equal to the number of beams used to probe the subband rather than the number of elements in the sensor array. The MUSIC algorithm can be applied to this noise subspace. Alternatively, a transformation matrix can be computed a priori, which maps the beamspace signal eigenvectors to the corresponding signal subspace that has the ESPRIT structure. The TLS-ESPRIT algorithm is then modified to obtain the two directions for each source from a single eigenvalue-eigenvector pair. Hence, they are automatically coupled