An EM‐based adaptive multiple target tracking filter

Tracking targets of interest is one of the major research areas in radar surveillance systems. We formulate the problem as incomplete data estimation and apply EM to the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The advantage is that the measurement‐update deals with multiple measurements in parallel and the parameter‐update estimates the system parameters on the fly. Experiments tracking separate targets in parallel show that tracking maintenance ratio of the proposed system is better than that of NNF and RMS position error is smaller than that of PDAF. Also, the system parameters are correctly obtained even from incorrect initial values. Copyright © 2001 John Wiley & Sons, Ltd.     

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.     

EM procedures using mean field-like approximations for Markov model-based image segmentation

Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments.     

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.     

一种放射CT重建的鲁棒估计算法

放射CT的重建问题可以归结为一种基于泊松分布的最大似然估计（MuxnimumLikelihood,ML），其中最著名的如EM算法[1]，这种算法存在的问题是鲁棒性不强，当观测数据中存在一些出格点（outlier）（如2％）时，EM算法的性能就变得很差。本文分析了算法性能变差的原因，将泊松分布近似为高斯分布后，仿照鲁律估计中的M-估计，构造了一种鲁棒性很强的EM改进算法。实验结果表明，即使在投影数据中混有10％的出格点，仍能重建出满意的结果来。     

改进的FD—TD法分析二维理想导体TM近区散射问题

本文针对一般ＦＤ－ＴＤ方法分析计算二维理想导体散射问题所遇到散射体边角处难以精确处理的缺点进行了改进。将边角处总场近似解析解直接引入ＦＤ－ＴＤ法差分公式，得到了有关修正系数，为了检验此方法的有效性，有无限长导体方柱为例分别用一般ＦＤ－ＴＤ法和本文的ＦＤ－ＴＤ方法进行了分析研究，并与ＭＯＭ法进行了比较，所得结果说明改进后的ＦＤ－ＴＤ方法对分析计算导体边角附近电流分布特性是较有效的。     

羰基铁类随机混合吸波材料等效电磁参数的计算

本文为计及多重散射偶极子间的相互作用，引入参量εｈ和μｈ，导得一组公式。它不仅能计算铁氧体类也能计算羰基铁类的随机混合吸波材料的等效电磁参数，均与实验结果吻合良好。     

EM发酵饲料中主要微生物类群的动态变化

对EM发酵饲料中乳酸杆菌、酵母菌、大肠菌群、腐败菌等微生物类群的动态变化进行了研究。结果表明 :饲料经EM发酵后 ,乳酸杆菌大量增加 ,发酵第4天达到最大值(9.5×108CFU·g-1 饲料) ,大肠菌群与腐败菌被明显抑制。并认为细菌总数与酵母菌数量不能作为评价EM发酵饲料质量好坏的指标。     

SAS数据挖掘在冷轧酸洗卷质量缺陷分析中的应用

数据挖掘作为一种发现大量数据中潜在信息的数据分析技术,受到各界的密切关注。SAS数据挖掘技术是众多数据挖掘方法中的佼佼者,它在大型企业中得到很好的应用。本文介绍数据挖掘的背景知识,并利用SAS/EM工具,对该技术在冷轧酸洗卷质量缺陷原因分析作了初步尝试。     

Deconvolution with partially known kernel of nonnegative signals

This paper extends the Bayesian direct deconvolution method to the case in which the convolution kernel depends on few unknown nuisance parameters. Moreover, an acceleration procedure is proposed that drastically reduces the computational burden. Finally, the implementation of the method by means of the fast Fourier transform is fully discussed in the multidimensional case.