最小均方误差光强估计技术

开关键控直接检测光通信系统需要对信号光强进行估计，提出了利用最小均方误差估计技术，并通过仿真同最大似然估计进行了比较，可以看出最小均方误差估计器在弱湍流情况下优于最大似然估计器。     

基于导频的OFDM信道估计方法及分析

对OFDM系统中基于导频的信道估值器进行了比较分析,分析了DVB-T系统中导频的分布,在DVB-T系统基础上结合最大似然准则和最小均方误差准则,提出了信道估计的方法,并在Reyleigh信道模型下,运用Matlab实现仿真.     

MC DS-CDMA系统中信道估计器的比较

首先介绍了几种多载波码分多址(CDMA)系统，然后建立了MCDS-CDMA系统模型，在该系统中进行最小二乘(LS)，线性最小均方误差(LMMSE)和最大似然(ML)3种信道估计器的研究，并对其均方误差(MSE)和误码率(BER)性能进行了仿真，LS实现最简单，但性能最差，LMMSE计算量大，性能优于LS，ML性能最佳，且其计算复杂度低于LMMSE。     

LTE中基于发送分集预编码的译码算法

主要研究了LTE系统中基于发射分集预编码的接收端译码算法。提出了在接收端采用的最大似然（ML）译码算法和最小均方误差（MMSE）译码算法,对这2种译码算法原理进行了分析,并对其性能进行了仿真和比较。仿真结果表明最大似然译码算法的性能比最小均方误差译码算法的性能要好,但LTE中基于空间复用的预编码在接收端可以采用最小均方误差算法进行译码,因此,如果接收端采用最小均方误差译码方案就可以对2种预编码方案进行译码,简化接收端译码的复杂度。     

基于声门闭合瞬间检测的时延算法研究

阐述了基于声门闭合瞬间检测的时延估计方法基本原理,研究了基于希尔伯特变换的线性预测残留误差时延估计法提取时延的方法,并与最大似然广义互相关方法进行了性能分析和比较,结果表明线性预测残留误差的均方偏差最小,抗混响能力强,运算量适中,便于实时实现。     

一种码片内多径参数的最大似然估计算法

针对码分多址卫星移动通信中码片内两径参数估计问题,该文提出了基于最大似然的2维搜索算法。算法对两径时延进行2维搜索,同时对多径复振幅分别采用解相关和线性最小均方误差(LMMSE)算法进行估计,最后通过使似然函数最大化而找出最优的一组时延、复振幅估计值。仿真证明两径时延2维搜索结合采用近似的LMMSE算法进行多径复振幅估计具有良好的性能。     

信息理论与技术

0009602高动态多普勒频率的最大似然估计器〔刊〕/王晓湘//北京邮电大学学报.—2000,23(1).—61～65(K)采用泰勒级数展开的形式表示高动态的多普勒频率参数,推导并实验分析了对各阶频率参数估计的最大似然估计器(MLE);计算机模拟采用 FFT 处理方法,给出了算法对所模拟的接收机动态的估计均方根误差和失锁概率。参3     

DVB-T系统中信号的迭代检测方法

针对多径信道环境下的DVB-T系统,给出了数学期望最大(EM)算法迭代检测发送信号的方法,初始信道的频率响应采用最大似然(ML)和最小均方误差(MMSE)方法进行估计.由于该方法需要知道信道的统计特性,因此还给出了信道协方差矩阵和当前噪声功率的估计方法.仿真结果表明,利用估计的信道统计信息,系统的性能可以接近理想信道的性能.     

蜂窝无线通信中的时空信号处理技术

论述了蜂窝无线通信中的空时信号处理方法,给出了空时信号处理的2个最优准则空时最大似然序列估计(ST-MLSE)准则和空时最小均方误差估计(ST-MMSE)准则,并给出了两种最优准则的粗略性能比较.另外,对无线信道特性作了一些简要说明.     

基于正交训练序列的MIMO系统联合最大似然时频同步和信道估计

该文推导了多输入多输出(MIMO)系统中的符号定时、频偏和信道参数的联合最大似然(ML)估计。针对联合ML估计没有闭合的表达式、数值计算复杂度高的问题,该文提出了一种基于重复结构的正交训练序列的简化估计算法。该估计算法形式简单、复杂度低,且仍为最大似然估计。最后仿真分析了最大似然参数估计的均方误差与接收信噪比和天线数目的关系,并与Cramer-Rao界作了比较,表明了该算法的有效性。     

Estimation of P[Y

Kundu  D. Gupta  R.D. 《Reliability, IEEE Transactions on》2006,55(2):270-280

A MAP Estimator for the m Parameter in Nakagami Fading Ultra-Wide Bandwidth Indoor Channels

A maximum a posteriori (MAP) estimator for the Nakagami m parameter in an ultra-wide bandwidth (UWB) indoor channel is proposed. Previous work exclusively studies maximum likelihood (ML) estimation and moment method (MM) estimation of the Nakagami m parameter. This letter derives the MAP estimator for the Nakagami m parameter by using the a priori probabilities of the Nakagami fading parameters in an indoor UWB channel. The performance of the MAP estimator is examined and compared with those of the ML estimator and the MM estimator. Numerical results demonstrate that the new MAP estimator is superior to the ML estimator and the MM estimator in an indoor UWB channel, especially when the sample size in the estimation is small     

DVB-S2 ACM技术中的信噪比估计

为了在DVB-S2系统自适应编码调制(ACM)技术中实现对信道信噪比的精确、高效估计,提出了一种基于子空间分解的数据辅助类信噪比估计算法。计算机仿真结果表明,在较宽的信噪比范围内,该算法的性能优于数据辅助类的最大似然估计算法和经典的基于子空间分解的ED算法,估计精度高,计算复杂度低,非常适合在DVB-S2系统ACM技术中应用。     

An iterative algorithm for single-frequency estimation

An algorithm for the estimation of the frequency of a complex sinusoid in noise is proposed. The estimator consists of multiple applications of lowpass filtering and decimation, frequency estimation by linear prediction, and digital heterodyning. The estimator has a significantly reduced threshold relative to existing phase-based algorithms and performance close to that of maximum likelihood estimation. In addition, the mean-squared error performance is within 0.7 dB of the Cramer-Rao bound (CRB) at signal-to-noise ratios (SNRs) above threshold. Unlike many autocorrelation and phase-based methods, the proposed algorithm's performance is uniform across a frequency range of -/spl pi/ to /spl pi/. The computational complexity of the algorithm is shown to be favorable compared with maximum likelihood estimation via the fast Fourier transform (FFT) algorithm when significant zero-padding is required.     

一种改进型CE-ML算法的二维DOA估计

基于最大似然估计（ML）的阵列测向方法具有测向精度高、可以分辨相干信号等优点,但是因为计算复杂度过高而工程应用受限。针对该问题,利用交叉熵（CE）方法对最大似然估计快速求解,并对初始样本的产生和平滑参数的设置进行了优化,提出改进型CE—ML二维测向算法,最后进行了算法运算量分析和仿真验证。仿真实验表明,在精度相近条件下,改进型的CE-ML算法的迭代次数大约是粒子群算法（Ps0）的1／3,大大减少了ML测向的计算量。     

Position‐based adaptive quantization for target location estimation in wireless sensor networks using one‐bit data

The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one‐bit information to its fusion center. To improve the performance of estimation, a position‐based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position‐based information sequences and then sends its one‐bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position‐based maximum likelihood estimator as well as its corresponding position‐based Cramér–Rao lower bound are derived. Numerical results show that the position‐based maximum likelihood estimator is more accurate than the classical fixed‐quantization maximum likelihood estimator and the position‐based Cramér–Rao lower bound is less than its fixed‐quantization Cramér‐Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.     

一种非判决辅助前向结构载波频差估计方法

本文从最大似然准则出发,提出一种适于旋转对称星座的非判决辅助前向结构载频偏差估计算法.为了解决相位区间跳变问题,从而扩大估计范围,提出了一种新的相位展开方法.对于2π/M旋转对称星座,该估计算法的估计范围可扩大到±1/2MT.计算机仿真给出了该算法在估计范围、估计精度以及计算复杂度方面的性能.     

Error probability estimator structures based on analysis of thereceiver decision variable

The paper presents schemes for rapid on-line error probability estimation of digital communications links. Several estimator structures are proposed based on the assumption of sample independence, including weighted least squares (WLS) and maximum likelihood (ML) forms. The continuous-form ML estimator is shown to lie on the Rao-Cramer bound, making it a most efficient estimator of probability of error. The design, performance, implementation complexity and behavior of these estimators is described for AWGN     

EM/SAGE Based ML Channel Estimation for Uplink DS-CDMA Systems over Time-Varying Fading Channels

The matrix inversion for the maximum likelihood (ML) channel estimation requires high complexity for the direct-sequence code-division multiple-access (DS-CDMA) systems. The prime motivation of the paper is to propose channel estimators that achieve mean square error (MSE) performance of ML channel estimator in an iterative manner without any matrix inversion. Therefore, two computationally efficient solutions to the problem of ML channel estimation are proposed.We compare the both algorithms in terms of the number of used iteration and show that the proposed algorithms converge the same MSE performance of the ML estimator as the increasing number of iterations.