多传感器观测下带乘性噪声系统的逆向滤波与反褶积融合算法     

褚东升  李玉全  梁猛  张玲 《中国海洋大学学报(自然科学版)》2002,32(5):837-842

针对多传感器观测环境下带乘性噪声系统的逆向最优滤波与反褶积融合估计问题 ,本文提出了 1种基于极大似然准则的最优融合算法。该算法中各单传感器间并行计算 ,并且融合中心与单传感器处理中心间无反向通讯 ,因而执行效率较高。仿真表明 ,该融合算法产生的逆向滤波与反褶积比单传感器处理结果有较明显提高  相似文献   

Modelling the size selectivities of a trawl codend and an associated square mesh panel   总被引：8，自引：1，他引：8  

Zuur  G.; Fryer  R. J.; Ferro  R. S. T.; Tokai  T. 《ICES Journal of Marine Science》2001,58(3):657-671

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一种优化模糊度搜索方法的研究   总被引：1，自引：0，他引：1  

刘立龙  文鸿雁  唐诗华 《海洋测绘》2006,26(1):37-39,53

对于高精度测量和导航,GPS载波相位整周模糊度的快速求解仍然是一个难点,尤其对于单频接收机。提出一种快速求解整周模糊度的方法,其基本思想采用分步求解,首先应用最小二乘模糊去耦调节法(LAMBDA)搜索出来的模糊度作为初始值,然后应用卫星分组方法降低搜索维数,并应用极大似然准则,构造搜索函数,最后应用最优化原理,搜索出最优的模糊度参数,并从三个方面对其进行检验,即RATIO检验,OVT检验,多项式拟合残差检验。为验证该算法,我们用单频GPS接收机进行了实验,利用本文方法在11 S以内正确确定了模糊度,其基线长误差小于3MM,表明该方法不但可以改进模糊度的搜索速度,而且可以进一步提高其可靠性和成功率。该方法可广泛应用于定向及姿态测量。  相似文献   

基于分布式检测与决策融合的海底油气管线检漏   总被引：3，自引：0，他引：3  

白莉  岳前进崔莉 《海洋工程》2006,24(1):55-59

利用多传感器的信息融合思想，提出分布式检测与决策融合方法进行长距离海底管线泄漏监测。试验水管和天然气／凝析油管线现场数据分析表明此算法不仅降低了误报警概率，且对流体在输送过程中动态变化具有稳健性。  相似文献   

Estimation of gillnet and hook selectivity using log-linear models   总被引：5，自引：0，他引：5  

Millar  Russell B.; Holst  Rene 《ICES Journal of Marine Science》1997,54(3):471-477

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Estimation of covariance matrix from geochemical data with observations below detection limits     

Chang-Jo F. Chung 《Mathematical Geology》1993,25(7):851-865

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.  相似文献   

Estimation for partially observed Markov processes     

M. E. Thompson  T. N. Kaseke 《Stochastic Hydrology and Hydraulics》1995,9(1):33-47

Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (X_n, n 0) or (X_t, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Y_n, n 0) or (Y_t, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points.  相似文献   

GIS叠置图层方差分量的极大似然估计   总被引：1，自引：0，他引：1  

王新洲  游扬声  刘星  史文中 《武汉大学学报(信息科学版)》2005,30(10):892-896

针对GIS叠置中的同名点,以维希特分布密度为似然函数,提出了各图层方差分量的极大似然估计方法。该方法不依赖残差,不需要迭代就能估计未知参数和方差分量。  相似文献   

A variance-ratio test for supporting a variable mean in kriging     

Peter K. Kitanidis 《Mathematical Geology》1997,29(3):335-348

This article discusses the issue of whether to use a variable mean and describes a test that can be used to evaluate whether it is justified to add terms to the drift (deterministic part) of a geostatistical model. The basic model could be the intrinsic one, where the deterministic part is a constant, and the alternate model could be any model that includes a constant term in the expression for the drift. Also, differences between constant- and variable-mean models are discussed.  相似文献   

Extreme value theory based on the r largest annual events: a robust approach     

D. J. Dupuis 《Journal of Hydrology》1997,200(1-4):295-306

In Smith (1986, J. Hydrol. 86, 27–43), a family of statistical distributions and estimators for extreme values based on a fixed number r > = 1 of the largest annual events are presented. The method of estimation was numerical maximum likelihood. In this paper, we consider the robust estimation of parameters in such families of distributions. The estimation technique, which is based on optimal B-robust estimates, will assign weights to each observation and give estimates of the parameters based on the data which are well modeled by the distribution. Thus, observations which are not consistent with the proposed distribution can be identified and the validity of the model can be assessed. The method is illustrated on Venice sea level data.  相似文献