MAP symbol estimation on frequency-flat Rayleigh fading channelsvia a Bayesian EM algorithm

In this paper, an expectation-maximization (EM) technique for maximum a posteriori estimation is employed to devise novel soft-in soft-out algorithms for symbol detection over frequency-flat Rayleigh fading channels. An application of these algorithms to iterative decoding of coded PSK signals is proposed and some performance results are illustrated     

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.     

用于高斯混合模型参数估计的EM算法及其初始化研究

基于有限混合模型的聚类是一种重要的聚类分析方法,而EM算法是混合模型参数估计的重要方法.传统的EM算法对初始聚类中心比较敏感,因此如何选取初始值成为运用EM算法实现高斯混合模型聚类中的一个重要问题.本文提出一种基于网格的聚类算法来初始化EM算法,旨在改善EM算法的初始敏感性,使其达到更佳的聚类效果.此算法根据网格单元密...     

Kernel-based deterministic annealing algorithm for data clustering

Data clustering in kernel-induced feature space is interesting in that, by nonlinearly mapping the observed data from a low-dimensional input space into a high (possibly infinite)-dimensional feature space by means of a given kernel function, the kernel-based clustering can reveal complicated (e.g. linearly nonseparable) data structures that may be missed by traditional clustering methods in the standard Euclidean space. A kernel-based deterministic annealing (KDA) algorithm is developed for data clustering by using a Gaussian kernel function. The Gaussian parameter (width), which determines the nonlinear mapping together with the Gaussian kernel, is adaptively selected by the scaled inverse of data covariance. The effectiveness of the Gaussian parameter (width) selection method and the superiority of the KDA algorithm for clustering a variety of data structures are supported by the experimental results on artificial and real data sets     

Robust parameter estimation of a deterministic signal in impulsivenoise

This paper presents a robust class of estimators for the parameters of a deterministic signal in impulsive noise. The proposed technique has the structure of the maximum likelihood estimator (MLE) but has an extra degree of freedom: the choice of a nonlinear function (which is different from the score function suggested by the MLE) that can be adjusted to improve robustness. The effect of this nonlinear function is studied analytically via an asymptotic performance analysis. We investigate the covariance of the estimates and the loss of efficiency induced by nonoptimal choices of the nonlinear function, giving special attention to the case of α-stable noise. Finally, we apply the theoretical results to the problem of estimating the parameters of a sinusoidal signal in impulsive noise     

Model-based parameter estimation in electromagnetics. II.Applications to EM observables

Discusses spectral domain model based parameter estimation (MBPE), waveform-dominated MBPE, and adapting and optimizing the sampling of the generating model. These are discussed with reference to antenna theory including scattering     

波形未知信号时频差参数的最大似然估计算法

提出了波形未知确定信号时频差参数估计的最大似然算法,得出利用互模糊函数可以实现此种信号条件下时频差参数最大似然估计的结论,推导出了闭合形式的克拉美罗下界并对比了不同信号模型下的参数估计精度。仿真实验结果表明了估计算法和性能分析的正确性。     

Model-based parameter estimation in electromagnetics. III.Applications to EM integral equations

For pt.II see ibid., vol.40, no.2, p.51-66 (1998). This article has considered the rationale and illustrated the application of model-based parameter estimation (MBPE) to achieve reduced-order representations of electromagnetic observables via fitting models, the model-based part of MBPE, that derive from the physics of EM fields. The parameter-estimation part of MBPE is the process of obtaining numerical values for the coefficients of the fitting model by matching or fitting it to sampled values of the EM observable of interest. Although a wider range of fitting models are feasible, attention here is focused on what are termed waveform-domain models, comprised of exponential series, and spectral-domain models, comprised of pole series. These kinds of fitting models are shown to provide natural basis functions for many kinds of EM observables, whether these observables are based on experimental measurement or numerical computation     

EM框架下实现被动测距的状态估计和参数学习

在红外成像跟踪系统中,通常仅能测量目标的角度信息,不能直接测量目标与观测站间的距离。研究了基于红外成像系统的被动测距技术,首先利用状态空间模型的分析方法建立被动测距的状态估计和参数学习的混合估计模型,然后介绍EM的基本原理和参数的最大似然估计。EM算法的E步利用粒子滤波和粒子平滑器来完成,实现被动测距的状态估计;M步利用梯度搜索的方法来求解参数。被动测距是一个带有未知参数的非线性系统的状态估计,文中利用状态估计与参数学习的状态空间模型来描述,并利用EM法来求解,为被动测距的求解提供了一条新的途径。模拟实验表明,基于粒子滤波和梯度搜索的EM方法能同时完成被动测距的状态估计和参数学习。     

MAP译码算法对信道估计偏差敏感度分析

作为MAP译码算法的必要参数,信噪比通常通过信道估计单元得到。相关研究表明,信噪比估计值偏差对Turbo译码器输出性能将造成较大的影响。通过对MAP算法的求解,从理论上解释了MAP译码性能下降的主要因素,并通过理论分析和仿真验证,论证了当估计值较真实值小时译码性能明显降低的原因,同时证明了长帧Turbo码对信噪比参数更为敏感。     

Nonlinear parameter estimation via the genetic algorithm

A modified genetic algorithm is used to solve the parameter identification problem for linear and nonlinear IIR digital filters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The scheme is also applied to feedforward and recurrent neural networks     

Application of the EM algorithm for the multitarget/multisensortracking problem

An important problem in surveillance and reconnaissance systems is the tracking of multiple moving targets in cluttered noise environments using outputs from a number of sensors possessing wide variations in individual characteristics and accuracies. A number of approaches have been proposed for this multitarget/multisensor tracking problem ranging from reasonably simple, though ad hoc, schemes to fairly complex, but theoretically optimum, approaches. In this paper, we describe an iterative procedure for time-recursive multitarget/multisensor tracking based on use of the expectation-maximization (EM) algorithm. More specifically, we pose the multitarget/multisensor tracking problem as an incomplete data problem with the observable sensor outputs representing the incomplete data, whereas the target-associated sensor outputs constitute the complete data. Target updates at each time use an EM-based approach that calculates the maximum a posteriori (MAP) estimate of the target states, under the assumption of appropriate motion models, based on the outputs of disparate sensors. The approach uses a Markov random field (MRF) model of the associations between observations and targets and allows for estimation of joint association probabilities without explicit enumeration. The advantage of this EM-based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum approaches that use explicit enumeration of the joint association probabilities. We provide selected results illustrating the performance/complexity characteristics of this EM-based approach compared with competing schemes     

Design of discrete coefficient FIR filters by a fast entropy-directed deterministic annealing algorithm

We present an entropy-directed deterministic annealing optimization algorithm and show its applicability to the problem of designing digital filters with discrete coefficients, each implemented as a sum of signed power-of-two terms and additional general hardware constraints. The algorithm is based on analogies from statistical mechanics and is related to the well-known mean field annealing algorithm. It utilizes estimates of conditional entropy to prune the problem during the optimization, thereby reducing the computational time by 30 to 50%. In conjunction with a scheme to compute the value of the objective function as a sequence of updates, this approach leads to a very fast algorithm. As an application example demonstrating the potential of the new method, we consider the design of digital filters with discrete coefficients consisting of a minimum number of signed power-of-two terms.     

DPA: a deterministic approach to the MAP problem

Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.     

改进的高效MIMO-OFDM系统EM信道估计算法

针对MIMO-OFDM系统中期望最大化(EM)信道估计算法在高信噪比(SNR)下带来的误差地板(EF)现象,且OFDM符号的数据传输效率随发射天线数的增加而明显降低,提出一种改进的高效EM信道估计算法。该算法首先引入一种准确的等效信号模型并推导出一种修正的EM算法,改善了EM算法在高SNR下的性能;在多个OFDM间利用相位正交导频序列来提高数据传输效率,同时进行联合信道估计以提高估计性能。仿真实验验证了所提算法具有更高的信道估计性能和更高的数据传输效率。     

烟花算法在SAR图像属性散射中心参数估计中的应用

针对合成孔径雷达（SAR）属性散射中心估计问题,提出基于烟花算法的方法。首先,在图像域对SAR图像中高能量区域进行分割解耦,获得单个独立散射中心在图像域的表现形式。在此基础上,以属性散射中心参数化模型为基础,构建优化问题,对分离出来的单个散射中心进行最优参数的搜索。在此阶段,引入烟花算法进行参数寻优。该算法具有强大的全局和局部搜索能力,在保证优化精度的条件下避免陷入局部最优,从而保证散射中心参数估计的可靠性。在原始图像中剔除求解后的单个散射中心,对残余图像进行高能量区域分割,序惯估计下一个散射中心的属性参数。最终,获取输入SAR图像上所有散射中心的参数集。实验中,首先基于MSTAR数据集中的SAR图像进行参数估计验证,通过参数估计结果与原始图像的对比以及基于估计参数集对原始图像进行重构,反映了提出算法的有效性。此外,实验还基于估计得到的属性参数进行SAR目标识别算法验证,通过与其他参数估计算法在相同条件下进行识别性能的对比,进一步体现了提出方法在属性散射中心参数估计上的性能优势。     

Joint parameter/state estimation

It is shown how the refined instrumental variable method of parameter/state estimation for single-input single-output (s.i.s.o.) systems proposed by Young1 can be extended to multi-input multi-output (m.i.m.o.) systems. As might be expected, the extension follows directly from the s.i.s.o. analysis, but involves some difficult and interesting matrix manipulations.     

On the influence of a detection step on lower bounds for deterministic parameter estimation

A wide variety of actual processing requires a detection step, whose main effect is to restrict the set of observations available for parameter estimation. Therefore, as a contribution to the theoretical formulation of the joint detection and estimation problem, we address the derivation of lower bounds for deterministic parameters conditioned by a binary hypothesis testing problem. The main result is the introduction of a general scheme-detailed in the particular case of CRB-enabling the derivation of conditional deterministic MSE lower bounds. To prove that it is meaningful, we also show, with the help of a fundamental application, that the problem of lower bound tightness at low SNR may arise from an incorrect lower bound formulation that does not take into account the true nature of the problem under investigation: a joint detection-estimation problem.     

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。     

伪随机二相码连续波信号参数估计算法

提出了一种伪随机二相码连续波信号参数估计算法。利用倍频法估计出载频和初相,由估计的载频和初相构造相关接收机,根据相关接收机的输出估计码元宽度、码元个数和码序列。