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     

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     

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。     

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     

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.     

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.     

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.     

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。     

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.     

Parameter estimation of incomplete data in competing risks using the EM algorithm

Consider a system which is made up of multiple components connected in a series. In this case, the failure of the whole system is caused by the earliest failure of any of the components, which is commonly referred to as competing risks. In certain situations, it is observed that the determination of the cause of failure may be expensive, or may be very difficult to observe due to the lack of appropriate diagnostics. Therefore, it might be the case that the failure time is observed, but its corresponding cause of failure is not fully investigated. This is known as masking. Moreover, this competing risks problem is further complicated due to possible censoring. In practice, censoring is very common because of time and cost considerations on experiments. In this paper, we deal with parameter estimation of the incomplete lifetime data in competing risks using the EM algorithm, where incompleteness arises due to censoring and masking. Several studies have been carried out, but parameter estimation for incomplete data has mainly focused on exponential models. We provide the general likelihood method, and the parameter estimation of a variety of models including exponential, s-normal, and lognormal models. This method can be easily implemented to find the MLE of other models. Exponential and lognormal examples are illustrated with parameter estimation, and a graphical technique for checking model validity.     

一种适于衰落信道的ZPOFDM参数盲估计算法

提出了一种基于循环累积量的ZPOFDM参数盲估计算法,完成了衰落信道下ZPOFDM包括符号周期、零前缀长度,子载波数等多个参数的估计.文章推导与分析了ZPOFDM信号二阶循环累积量表达式,指出其截面的谱线特征,给出了算法步骤,并对估计性能进行了详细讨论.实验结果表明,在频率选择性衰落信道下,较已有方法,本文算法估计特征...     

A combined order selection and parameter estimation algorithm forundamped exponentials

We propose an approximate maximum likelihood parameter estimation algorithm, combined with a model order estimator for superimposed undamped exponentials in noise. The algorithm combines the robustness of Fourier-based estimators and the high-resolution capabilities of parametric methods. We use a combination of a Wald (1945) statistic and a MAP test for order selection and initialize an iterative maximum likelihood descent algorithm recursively based on estimates at higher candidate model orders. Experiments using simulated data and synthetic radar data demonstrate improved performance over MDL, MAP, and AIC in places of practical interest     

模拟退火算法在单目标规划问题中的应用

模拟退火算法是一种用于解决连续、有序离散和多模态优化问题的随机优化技术。它对于非常复杂，高度非线性的大型系统优化的求解，表现出比其他传统优化算法更加独特和优越的性能。现介绍了模拟退火算法的原理、数学模型及其求解步骤，并以一实例来说明模拟退火算法在解决组合优化问题时的有效性和优越性。     

Channel parameter estimation in mobile radio environments using theSAGE algorithm

This study investigates the application potential of the SAGE (space-alternating generalized expectation-maximization) algorithm to jointly estimate the relative delay, incidence azimuth, Doppler frequency, and complex amplitude of impinging waves in mobile radio environments. The performance, i.e., high-resolution ability, accuracy, and convergence rate of the scheme, is assessed in synthetic and real macro- and pico-cellular channels. The results indicate that the scheme overcomes the resolution limitation inherent to classical techniques like the Fourier or beam-forming methods. In particular, it is shown that waves which exhibit an arbitrarily small difference in azimuth can be easily separated as long as their delays or Doppler frequencies differ by a fraction of the intrinsic resolution of the measurement equipment. Two waves are claimed to be separated when the mean-squared estimation errors (MSEEs) of the estimates of their parameters are close to the corresponding Cramer-Rao lower bounds (CRLBs) derived in a scenario where only a single wave is impinging. The adverb easily means that the MSEEs rapidly approach the CLRBs, i.e., within less than 20 iteration cycles. Convergence of the log-likelihood sequence is achieved after approximately ten iteration cycles when the scheme is applied in real channels. In this use, the estimated dominant waves can be related to a scatterer/reflector in the propagation environment. The investigations demonstrate that the SAGE algorithm is a powerful high-resolution tool that can be successfully applied for parameter extraction from extensive channel measurement data, especially for the purpose of channel modeling