基于STBC-SCFDE系统的信道估计算法研究

针对频率选择性衰落信道下2发射天线的空时分组编码-单载波频域均衡(STBC-SCFDE)系统,分别设计了2种新颖的时域多天线导频序列和频域多天线导频序列,相应地提出了一种时域导频信道估计算法和一种频域导频信道估计算法,并分别进行了信道估计的MSE性能分析以及计算复杂度对比.仿真验证表明,相比传统经典多天线信道估计算法,所提2种信道算法在中低信噪比下均能实现良好的信道估计性能,为未来无线通信系统的信道估计开辟了新的解决途径.     

OFDM系统基于导频的信道多径时延估计

本文针对宽带正交频分复用(OFDM)系统,分析了多径时延扩展与循环前缀(cP)长度及导频数目的关系.随后利用导频的频域相关性估计得到信道时域功率时延谱(PDP),并根据PDP提出信道均方根(RMS)时延及最大多径时延门限值的估计算法.最终实现OFDM系统对信道多径参量的实时跟踪,有利于系统参数自适应调整.仿真结果表明,本文算法在低复杂度条件下可以有效获知当前传输信道模型多径时延参数的近似统计信息.     

OFDM系统中一种新的信道估计方法

在OFDM系统中,传统的基于导频的信道估计算法(如梳妆滤波器)都有一个共同的缺点,就是需要大量的导频符号,降低了系统的频谱效率。本文主要致力于研究新的信道估计算法,明确提出了将多径信道的时延估计和幅度估计分开的信道估计方案。对于时延估计,采用周期性插入块状导频估计时延。对于幅度估计,提出了一种全新的动态导频方式来估计多径的幅度,并在数学上证明了这种方法可以节省大量导频。仿真结果表明,本文提出的新算法和传统信道估计算法相比,可以节约至少百分之七十的导频,并且具有性能好,复杂度低的优点。     

OFDM系统基于信道多径时延检测的自适应信道估计

针对宽带正交频分复用(OFDM)系统,提出了一种基于信道多径时延估计的自适应信道估计算法.文章分析了无线信道的特性,随后利用导频频域相关性进行多径时延估计,提出通过动态跟踪信道时延估计参数自适应选择信道估计中适宜的导频数目的方法,同时实现循环前缀(CP)长度的实时调整.仿真结果表明,在时变环境下,本文算法可以有效获知当前信道时延参数的近似统计信息,实现对系统冗余信息的优化.     

基于LS的STBC导频信道估计方法

结合IEEE 802.16d(e)协议的相关内容,针对多天线载波分配和导频的排列方式,提出了一种简单的性能和复杂度折衷的导频信道估计算法,该算法是通过利用LS算法求得导频位置的信道估计,然后对导频信道估计进行滤波、插值来得到数据位置的信道估计.性能仿真显示,这种复杂度很低的算法BER性能损失是可以接受的,而且802.16标准规定系统导频只能用1个symbol,因此这种方法有其实用价值.     

一种适用于DVB-T系统的信道估计算法

提出了一种基于导频的信道估计算法,该算法在最小平方误差(LS)算法的基础上采用两维插值的方法估计出无线信道的频域响应,即在时域上进行线性插值,然后利用最大似然(ML)算法进行频域插值。在地面数字视频广播(DVB-T)系统中的仿真结果表明,该复合算法具有较高的性能。     

小波包多载波系统中的LMS信道估计

为了提高正交小波包波分复用(OWPDM)系统抗多径衰落和多普勒频移的性能,提出了一种新的半盲信道估计算法。首先利用时域滤波原理滤去信道响应中的噪声,之后利用信号在时域补零相当于频域内插的原理来获得更精确的最小均方算法(LMS)估计器迭代初值。仿真结果表明,在插入更少导频数目的情况下,新算法估计性能优于最小二乘(LS)信道估计算法,在插入相同数目导频的情况下,与传统的LMS信道估计算法相比,新算法能使系统获得更好的误码性能。     

MB-OFDM系统中采用梳状导频的信道估计

针对采用梳状导频的多频带正交频分复用(MB-OFDM)系统,提出一种新的基于频域插值定理的信道估计算法,即FD-INTERP.与传统的插值信道估计算法不同.FD-INTERP算法首先计算出导频点处的信道值,再根据DFT频域插值定理计算出时域信道.算法仅需较少的导频符号,提高了频带利用率,并且避免了插值计算,提高估计精度的同时降低了运算量.仿真结果表明该算法具有良好的可靠性和稳定性.     

高速移动通信系统中OTFS信道估计算法研究

针对高速移动环境中双色散信道会出现信道估计可靠性下降的问题,该文在正交时频空(OTFS)调制系统的输入-输出模型中提出一种基于压缩感知的信道估计算法.该算法利用信道中最大多普勒频移和最大时延确定导频发送矩阵的大小,相比传统的正交匹配追踪(OMP)信道估计算法,能够在保证相似信道估计准确度的情况下节省导频资源;并在此基础上,对OTFS调制符号做相位旋转,增加差分矩阵的秩,理论分析和仿真结果表明,该方案能够提升OTFS系统的分集阶数进而降低噪声的干扰.     

高效的基于数据与模型的信道估计算法

针对正交频分复用(OFDM)系统,提出一种新型的数据与模型联合驱动下的信道估计算法.该算法结合一种可在线训练的低复杂度学习型估计方法与线性最小均方误差(LMMSE)估计,既赋予信道估计器通过在线训练提升了估计性能的能力,又借助模型解决了在线生成训练数据会造成额外导频开销的问题,提升了系统效率.仿真结果表明,所提算法在低...     

The application of spectral estimation methods to bearing estimation problems

The equivalence between the problem of determining the bearing of a radiating source with an array of sensors and the problem of estimating the spectrum of a signal is demonstrated. Modern spectral estimation algorithms are derived within the context of array processing using an algebraic approach. Emphasis is placed on the problem of determining the bearing of a sound source with an array. Special issues encountered in applying these estimates are discussed.     

Wavelet estimation

An important problem in seismic exploration is the estimation of and correction for the seismic wavelet. A seismic signal may be modeled as a convolutional model with the wavelet as one component. The wavelet propagated by the seismic energy source is complicated by transmission and recording filters. Some filters in the system can be deterministically defined while others are more conjectural. The estimation of the wavelet is useful in two major ways. Borehole measurements are used to model the surface seismograms. The wavelet used in the model needs to match that of the seismogram to correlate the two measurements. Conversely, the estimated wavelet can be used to design inverse filters which make the seismogram approach the borehole measures. Some well-known methods for estimation of the wavelet are based on assumptions about the wavelet or the earth reflectivity. Examples of the methods indicate success on some data even though each makes different assumptions. The methods serve to point out basic problems in reliably estimating the wavelet from the seismogram. Basic problems include noise, band-limiting, nonstationarity, uncertain theoretical models, assumption failure, and widely diverse geological sequences of the earth. Quality control or evaluation of the performance of an estimation algorithm is a nontrivial problem. The estimation of the wavelet from a seismic recording remains an area of challenging research and importance in exploration for hydrocarbons.     

基于谱估计的Fabry-Perot标准具透过率曲线参数估计方法

直接探测多普勒测风激光雷达系统大多采用基于Fabry-Perot（F-P）标准具的条纹技术和边缘技术测量多普勒频移。但激光器频率漂移会使得透过率曲线的估计出现一定误差,为了抑制其影响,提出了一种基于谱估计的F-P标准具透过率曲线参数估计方法。首先将标准具全量程扫描结果进行重构得到观测矩阵。之后使用MUSIC算法得到透过率曲线的伪谱。对伪谱进行谱峰搜索后,将得到的谱峰位置进行线性拟合得到自由谱间距。然后将上步的拟合结果代入透过率函数,利用非线性最小二乘法对其他参数进行估计。在仿真分析的基础上,采用真实测量数据进行了实验验证。结果表明所提出的方法在扫描步点间隔达到50、信噪比大于10 dB的情况下,估计差小于1%。该算法扫描时间短,估计误差小,具有实际应用价值。     

Comparison on maximum-likelihood sequence estimation schemesincorporating carrier phase estimation

This paper compares the following two maximum likelihood sequence estimation schemes incorporating carrier phase estimation for coded MPSK: (1) the respective-state channel estimation and (2) the multiple differential detection. This paper confirms by analysis and computer simulation that both schemes have similar performance in the absence of intersymbol interference     

Structured estimation, II: Multivariate probability density estimation

We continue the research begun in 1975 on structured estimation. The original work in 1976 by Morgera and Cooper dealt with the Gaussian two-category classification problem when the common covariance matrix is unknown and must be estimated in order to approximate the hyperplane for decisionmaking, which is optimum for the true covariance matrix. We formulate the probability density function (pd0 estimation problem as a multivariate extension of the Rosenblatt-Parzen kernel method in which the multivariate characteristic function (cf) is estimated. A Gaussian form is assumed for the underlying probability distribution, and two methods are presented for the estimation of the covariance matrix in the cf: 1) a maximum-likelihood (MLE) general sample covariance matrix estimate, and 2) a constrained Toeplitz form estimate which takes full advantage of the structure imposed by weak stationarity of the underlying probability distribution. It is shown that both resulting cf estimates are asymptotically unbiased and consistent, albeit the structured covariance matrix estimate is itself only a {em first approximation to the MLE} and may not be positive definite. It is, however, apparently this difference in the estimators which gives rise to a considerable difference in finite sample sire performance. Typical calculations show that the effective sample size increase of the structured estimate can be considerable, a fact very important in nonparametric problems in which data are limited, or in which the sample size-to-dimensionality ratio is small. Applications of this research to the areas of nonparametric pattern recognition and communications theory are discussed.     

Adaptive estimation of eigensubspace

In a recent work we recast the problem of estimating the minimum eigenvector (eigenvector corresponding to the minimum eigenvalue) of a symmetric positive definite matrix into a neural network framework. We now extend this work using an inflation technique to estimate all or some of the orthogonal eigenvectors of the given matrix. Based on these results, we form a cost function for the finite data case and derive a Newton-based adaptive algorithm. The inflation technique leads to a highly modular and parallel structure for implementation. The computational requirement of the algorithm is O(N²), N being the size of the covariance matrix. We also present a rigorous convergence analysis of this adaptive algorithm. The algorithm is locally convergent and the undesired stationary points are unstable. Computer simulation results are provided to compare its performance with that of two adaptive subspace estimation methods proposed by Yang and Kaveh (1988) and an improved version of one of them, for stationary and nonstationary signal scenarios. The results show that the proposed approach performs identically to one of them and is significantly superior to the remaining two     

频率估计综述

频率估计是信息科学在信号处理领域的一个重要组成部分,在许多实际信号处理应用中具有非常重要的地位,且已广泛应用于雷达、地震、生物医学、语音处理和故障诊断等领域。文中归纳了频率估计的分析方法,并对主要的频率估计方法进行介绍和评述,最后对频率估计的发展方向进行了展望。     

一种LFM信号最大似然估计模型与参数估计快速算法

从最大似然估计模型入手,提出了一种适合在一般高斯噪声环境（包括色噪声）下LFM信号目标的参数估计模型和基于此估计模型的调频斜率和初始频率估计的快速算法。此方法获得了最大似然方法估计精度高的优点,且运算量比传统的最大似然方法大大降低。另外推导出了一般高斯环境下的LFM参数估计的CRB界,为一般高斯环境下的估计的参数的方差提供实际下界。     

WCDMA系统中基于多普勒频移估计的信道估计方法

Rake接收机是CDMA系统抗多径的有效手段，信道估计对其性能有很大的影响。传统方法是利用连续多个时隙的导频段估计出的信道参数，再对估计值进行平均。这种固定平均的方法不适合3G系统信道变化范围大的环境。本文提出了直接利用每个导频段的信道参数的包络来估计最大多普勒频移，再运用指数平滑法来调整各被估计时隙的权值的信道估计方法。仿真结果表明，该方法能准确有效地估计出信道参数，改善系统性能。     

Blind channel estimation via combining autocorrelation and blind phase estimation

Symbol spaced blind channel estimation methods are presented which can essentially use the results of any existing blind equalization method to provide a blind channel estimate of the channel. Blind equalizer's task is reduced to only phase equalization (or identification) as the channel autocorrelation is used to obtain the amplitude response of the channel. Hence, when coupled with simple algorithms such as the constant modulus algorithm (CMA) these methods at baud rate processing provide alternatives to blind channel estimation algorithms that use explicit higher order statistics (HOS) or second-order statistics (subspace) based fractionally-spaced/multichannel algorithms. The proposed methods use finite impulse response (FIR) filter linear receiver equalizer or matched filter receiver based infinite impulse response+FIR linear cascade equalizer configurations to obtain blind channel estimates. It is shown that the utilization of channel autocorrelation information together with blind phase identification of the CMA is very effective to obtain blind channel estimation. The idea of combining estimated channel autocorrelation with blind phase estimation can further be extended to improve the HOS based blind channel estimators in a way that the quality of estimates are improved.