一种改进的基于坡度变化的机载激光雷达点云滤波方法

机载激光点云数据滤波是获取高精度数字表面模型和数字高程模型的关键。本文分析了几种重要的滤波算法,在研究基于坡度变化的滤波算法的基础上,提出一种改进的分块滤波处理的方法。实验表明:该方法能有效对点云数据进行分类。     

平面约束条件在LIDAR点云滤波中的应用

介绍了一种利用平面约束条件对LIDAR点云数据进行滤波的方法,利用每个数据点的邻域点拟合平面,根据平面约束条件和平面点分类方法得到地面点,最后利用地面点内插该区域的DTM.     

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.

有色噪声作用下的卡尔曼滤波

利用GPS载波相位三差观测量进行动态定位(或精密导航),就必须研究有色噪声滤波的理论问题.根据需求,推导了动态噪声、观测噪声为有色噪声的线性系统滤波公式,并证明白噪声卡尔曼滤波是有色噪声卡尔曼滤波的特例,或者说有色噪声的卡尔曼滤波是白噪声卡尔曼滤波的推广.     

基于智能体的鄱阳湖区钉螺生长模型及其模拟研究

基于复杂自适应系统理论,采用自下而上的基于智能体的建模分析方法,模拟了自然环境与钉螺生长相互作用导致的钉螺生长的主要行为特征、生命状态和数量变化,开发实现了模拟模型,同时输入实测数据进行实例模拟,结果表明该模型能够作为血吸虫病防控工作中钉螺数量估测的一种辅助实验方法和工具.     

基于增强型GPS的自适应UKF实时星间相对定位方法

高精度的星间实时相对定位是卫星编队飞行的一项关键技术。本文以双星编队为例,提出一种基于GPS双频P码、双频载波相位以及星间距离观测信息的自适应UKF、实时相对定位方法。仿真结果表明:相比传统的EKF方法,该算法能有效地提高滤波稳定性及相对定位精度,而且,星间测距信息的引入能大大减少整周模糊度判定所需的历元数。     

用褶积滤波处理井水位对固体潮响应的滞后

本文给出了一种用褶积滤波处理井水位对固体潮响应滞后的新方法。鲁07井水位观测资料用这种方法处理比一般回归方法的平均中误差少4.6mm。文中讨论了褶积滤波积分区间长度与水井含水层渗透系数的关系及褶积滤波比别尔采夫滤波的优越性。     

1983年菏泽5.9级地震前后沂水泉氡的变化

沂水泉位于山东省沂水一汤头断裂带上。多年来的观测分析表明,影响沂水泉氡值变化的最大干扰是降雨。由于这种干扰存在着“记忆”滞后影响,所以用一般的相关分析难以将这种影响排除干净。本文对最小二乘法为建立模型的判据,用动态灰箱分析法拟合并推估观测系统的变化。在分析中用了四个状态变量：长趋势成分Ｌ（ｔ）、年周期变化成分Ｐ（ｔ）、降雨影响部分Ｒ（ｔ）和随机变化部分Ｓ（ｔ）。经过分析表明,沂水泉氡的长趋势部分１     

Efficient generation of conditional simulations by chebyshev matrix polynomial approximations to the symmetric square root of the covariance matrix

Consider the problem of generating a realization y₁ of a Gaussian random field on a dense grid of points ₁ conditioned on field observations y₂ collected on a sparse grid of points ₂. An approach to this is to generate first an unconditional realization y over the grid =_{1 2}, and then to produce y₁ by conditioning y on the data y₂. As standard methods for generating y, such as the turning bands, spectral or Cholesky approaches can have various limitations, it has been proposed by M. W. Davis to generate realizations from a matrix polynomial approximations to the square root of the covariance matrix. In this paper we describe how to generate a direct approximation to the conditional realization y₁, on ₁ using a variant of Davis' approach based on approximation by Chebyshev polynomials. The resulting algorithm is simple to implement, numerically stable, and bounds on the approximation error are readily available. Furthermore we show that the conditional realization y₁ can be generated directly with a lower order polynomial than the unconditional realization y, and that further reductions can be achieved by exploiting a nugget effect if one is present. A pseudocode version of the algorithm is provided that can be implemented using the fast Fourier transform if the field is stationary and the grid ₁ is rectangular. Finally, numerical illustrations are given of the algorithm's performance in generating various 2-D realizations of conditional processes on large sampling grids.     

Estimation for partially observed Markov processes

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.