基于EM算法的非高斯噪声参数估计

EM算法是一种从＂不完全数据＂中求解模型参数的极大似然估计的方法,在非高斯噪声的参数估计问题中是一种比较优秀的算法。非高斯噪声的参数估计问题的主要困难是充分统计量是不存在的,这意味着从观测空间到估计空间的映射依赖于这里试图估计的参数。在未知噪声概率密度的情况下,EM算法可以更准确地对非高斯噪声参数进行估计,估计方差接近C-R下界。     

基于独立混合模型的EM算法参数初始化实现方法

隐马尔科夫树( Hidden Markov Tree, HMT )的状态不能被观测到,只能观测到另一个与状态有联系的量,通过观测量估计HMT模型参数是一个不完全数据参数估计问题。期望最大化( Expectation Maximization, EM )算法是一种求参数极大似然估计的迭代算法,可以用于解决不完全数据参数估计问题,因此被广泛应用于HMT模型的参数估计中。当初始参数偏离真实参数较大时,EM算法迭代次数多,收敛速度慢,通过一个计算量不大的参数初始化处理,能够有效减少EM算法的迭代次数,加快收敛速度。本文提出了一种基于独立混合模型的参数初始化方法,详细介绍了该方法的实现过程,通过采用独立混合模型进行参数初始化,使得EM算法的迭代次数明显减少,收敛速度大大提高。最后,计算机仿真验证了该方法的可行性和有效性。      

期望最大(EM)算法及其在混合高斯模型中的应用

将期望最大(EM)算法应用于混合高斯模型中，通过对算法的介绍及其分析，得出EM算法是参数估计的一种有效算法，他大大降低了计算复杂度，但性能却与最大似然估计相近，具有很好的实际应用价值。     

基于EM算法的极大似然分布式量化估计融合新方法

该文针对水下目标探测中的多传感器分布式量化估计融合问题,建立了分布式量化估计融合模型,在考虑信道噪声且其统计特性不完全已知条件下,充分利用EM算法在观测数据缺失时参数估计的优越性,提出了一种基于期望极大化(EM)算法的极大似然分布式量化估计融合新方法。该方法将未知的水声信道噪声参数以及局部量化器量化概率建模为EM算法中二元高斯混合模型参数,利用极大似然估计方法的估计不变性得到目标参数的估计融合结果。仿真实验表明:该方法在局部传感器观测样本数目大于5000和信噪比大于6 dB时与已有理想信道条件下的估计方法性能相当,该方法为水下目标探测中分布式量化估计融合系统的工程实现提供了理论依据。     

结合EM/MPM算法和Voronoi划分的图像分割方法

为了实现在模型参数先验分布知识未知情况下进行基于区域和统计方法的图像分割,同时获取更精确的模型参数估计结果,提出了一种结合Voronoi划分技术、最大期望值(Expectation Maximization, EM)和最大边缘概率(Maximization of the Posterior Marginal, MPM)算法的图像分割方法。该方法利用Voronoi划分技术将图像域划分为若干子区域,待分割图像中的同质区域可以由一组子区域拟合而成,并假定同一同质区域内像素强度服从同一独立的正态分布,从而建立图像模型,然后结合EM/MPM算法进行图像分割和模型参数估计,其中,MPM算法用于实现面向同质区域的图像分割,EM算法用于估计图像模型参数。为了验证本文图像分割方法,分别对合成图像和真实图像进行了分割实验,测试结果的定性和定量分析表明了该方法的有效性和准确性。      

高斯混合噪声中弱信号的Rao检测方法

期望最大化(Expectation Maximization,EM)算法是求解参数最大似然估计(MLE)的最优迭代算法,但若参数初始化不恰当,会使估计值落入"初值陷阱",导致错误的参数估计值.为此,本文提出了估计高斯混合噪声参数的矩 - EM算法,即先求参数的矩估计,并用矩估计值初始化参数,再通过EM迭代算法估计参数.在此基础上,经高斯化滤波,导出了高斯混合噪声背景下未知幅度弱信号的Rao检验统计量.仿真结果表明,矩 - EM算法可以更准确地估计噪声参数;基于矩 - EM算法的Rao检测性能优于基于EM算法的Rao检测性能.     

稀疏重构混合源参数估计方法

针对近远场混合源定位问题,本文提出了一种基于稀疏信号重构的信源参数估计算法。该算法首先通过对接收信号的协方差矩阵进行稀疏重构估计出远场信源参数,接着采用协方差分离技术将近场源和远场源分离,最后利用均匀线阵的对称性和稀疏信号重构估计近场信源参数。该算法避免了二维谱峰搜索和近场源参数配对,也无需构造高阶累积量,降低了计算复杂度。仿真结果表明,该算法的空间分辨能力和混合源参数估计精度均高于基于子空间的混合源参数估计方法。      

一种同频重叠信号自适应盲分离算法

针对同频重叠信号的单通道盲分离问题,提出了一种自校正频偏和调整幸存路径数目的盲分离算法。针对两路QPSK信号同频重叠的且存在频率漂移等单通道信号盲分离,推导了基于过采样的信道参数估计模型,利用纠错编码的方法降低误码率,利用指数加权递归最小二乘法(RLS)估计信道参数,并利用估计出的信道参数校正频率漂移等带来的频率偏差,通过在逐幸存路径(PSP)算法调整幸存路径数目解决信道参数收敛和计算复杂度之间的矛盾,提高了频率漂移等变化信道下的参数估计性能,并降低了计算量。仿真结果表明,该算法能够较好地实现频偏自校正,弥补频率漂移带来的影响,同时可以大幅度降低计算量。     

基于最优解析树提取的多功能雷达状态快速估计方法

针对基于文法建模的多功能雷达(Multi-Function Radar,MFR)参数估计领域中常规算法具有的高运算复杂度问题,提出一种快速估计算法.该算法利用文法的派生过程仅与文法结构有关,而与文法概率参数无关这一事实,利用库克-杨-卡塞米(Cocke-Younger-Kasami,CYK)算法对截获雷达数据序列进行预处理,构造出可以反映该序列派生过程的解析表,进而从该解析表中提取出序列的最优解析树,然后利用改进的Viterbi-Score算法对雷达文法概率参数进行快速估计.论文仿真分析了该算法的计算复杂度、存储复杂度和估计精度,实验结果表明了该算法相对于常规算法,可以减少60%左右的计算量.     

基于周期宽带参考信号的直接定位方法

定位系统的模型误差是制约定位精度提高的最主要因素之一。目前大多数定位系统都采用参数标定或者参数差分的方法降低模型误差对定位精度的影响。本文针对辐射源的多星定位应用,采用低信噪比的周期宽带信号作为参考信号,对定位系统的模型误差进行校准。校准过程建立了模型误差参数直接估计模型和辐射源位置直接估计模型。模型误差直接估计模型中,建立起接收参考信号波形与模型误差参数之间的关系,运用最大似然方法获得超分辨率的模型误差参数估计。辐射源位置直接估计模型中,建立起模型误差参数、未知辐射源位置与接收未知信号波形之间的关系,直接估计出辐射源的位置。针对参考信号带宽大,周期长,信噪比低,导致模型误差参数估计计算复杂度大的问题,利用参考信号的周期特性对参考信号进行能量累积和分步搜索方法逐级引导,提高了系统参数估计速度和精度。最后给出一个仿真算例说明方法的有效性。      

New EM parameter estimation algorithm for a type of micro-motion SFM signal

Aiming at the problem that the existing method has high computational complexity and high SNR,a novel method of multi-component SFM signals parameter estimation based on EM algorithm was proposed as to spinning tar-get's narrow band micro-motion echoes.The echo model of spinning target and its micro-doppler measurements based on time-frequency analysis were given.The iterative parameter estimation steps based on Gaussian mixture model and EM algorithm to measurements were established.The simulation results demonstrate that when SNR is greater than -3 dB and the SFM components are equal or greater than 2,the method can estimate the target’s micro-motion parameters e.g.projection size accurately in narrow band condition.     

EM-based recursive estimation of channel parameters

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods     

The EM/MPM algorithm for segmentation of textured images: analysisand further experimental results

In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the EM/MPM algorithm.     

Wavelet-Based Semiblind Channel Estimation for Ultrawideband OFDM Systems

Ultrawideband (UWB) communications involve very sparse channels, because the bandwidth increase results in a better time resolution. This property is used in this paper to propose an efficient algorithm that jointly estimates the channel and the transmitted symbols. More precisely, this paper introduces an expectation-maximization (EM) algorithm within a wavelet-domain Bayesian framework for semiblind channel estimation of multiband orthogonal frequency division multiplexing based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding “insignificant” wavelet coefficients from the estimation process. Simulation results using UWB channels that were issued from both models and measurements show that, under sparseness conditions, the proposed algorithm outperforms pilot-based channel estimation in terms of the mean square error (MSE) and bit error rate (BER). Moreover, the estimation accuracy is improved, whereas the computational complexity is reduced compared with traditional semiblind methods.      

基于EM的主动段弹道导弹跟踪算法研究

为了提高未知弹道参数下主动段目标跟踪的精度,提出基于期望最大化（Expectation Maximization,EM）的联合优化算法框架.首先在E步基于平滑器得到状态和未知参数的后验估计,然后在M步计算初始状态的均值、协方差以及过程噪声协方差等未知统计量,最后推导出基于URTS（Unscented Rauch-Tung-Striebel）的EM算法,并给出未知统计量的最优解析解,避免了非凸优化难以求解的问题.仿真结果表明：在相同量级的计算量下,本文算法的状态估计精度优于迭代UKF（Unscented Kalman Filter）算法.     

Blind multiuser detection in uplink CDMA with multipath fading: a sequential EM approach

We consider joint channel estimation and data detection in uplink asynchronous code-division multiple-access systems employing aperiodic (long) spreading sequences in the presence of unknown multipath fading. Since maximum-likelihood (ML) sequence estimation is too complex to perform, multiuser receivers are proposed based on the sequential expectation-maximization (EM) algorithm. With the prior knowledge of only the signature waveforms, the delays and the second-order statistics of the fading channel, the receivers sequentially estimate the channel using the sequential EM algorithm. Moreover, the snapshot estimates of each path are tracked by linear minimum mean-squared error filters. The user data are detected by a ML sequence detector, given the channel estimates. The proposed receivers that use the exact expressions have a computational complexity O(2/sup K/) per bit, where K is the number of users. Using the EM algorithm, we derive low-complexity approximations which have a computational complexity of O(K/sup 2/) per bit. Simulation results demonstrate that the proposed receivers offer substantial performance gains over conventional pilot-symbol-assisted techniques and achieve a performance close to the known channel bounds. Furthermore, the proposed receivers even outperform the single-user RAKE receiver with Nyquist pilot-insertion rate in a single-user environment.     

应用加权高斯模型的非理想稀疏信道估计

在高速通信系统中,由于多径信道通常存在一些小的散射体,使得抽头向量不满足理想的稀疏特性,导致经典的稀疏估计算法存在一定的性能损失.针对上述非理想稀疏特性问题,提出了一种基于酉变换近似消息传递(Unitary Transform Approximate Message Passing,UT-AMP)和加权高斯(Weigh...     

基于灰色关联度的战场电磁环境复杂度模型

针对战场电磁环境复杂度计算中出现的贫信息、不确定性问题,提出了利用灰色关联度来分析战场电磁环境,并依据各参数的物理特性给出了指标序列的具体生成方法,从而得到了装备间的关联度模型和战场电磁环境复杂度计算模型,最后以仿真实例来验证了该模型的可行性及合理性。