Description: 本程序用matlab生成白噪声，并且基于一个离散线性随机系统的模型生成了y(k)和x(k)，绘制出了x(k|k-1)和x(k)的对比曲线，求出了提前一步预报的误差协方差阵的稳定值-the procedures used Matlab generate white noise, and on a discrete linear stochastic systems model generated y (k) and x (k), mapping out the x (k | k-1) and x (k) contrast curves, get a step ahead forecasting error covariance matrix of stable value Platform: |
Size: 1086 |
Author:孙磊 |
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Description: runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = initial covariance of observation sequence noise
returns model parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence Platform: |
Size: 7836 |
Author:西晃云 |
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Description: 本程序用matlab生成白噪声，并且基于一个离散线性随机系统的模型生成了y(k)和x(k)，绘制出了x(k|k-1)和x(k)的对比曲线，求出了提前一步预报的误差协方差阵的稳定值-the procedures used Matlab generate white noise, and on a discrete linear stochastic systems model generated y (k) and x (k), mapping out the x (k | k-1) and x (k) contrast curves, get a step ahead forecasting error covariance matrix of stable value Platform: |
Size: 1024 |
Author:孙磊 |
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Description: runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = initial covariance of observation sequence noise
returns model parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence Platform: |
Size: 7168 |
Author:西晃云 |
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Description: 探测三维冲突概率算法，通过对误差协方差矩阵的运算，对其进行一事实上的矩阵变换使算法进行简化，-Three-dimensional probabilistic conflict detection algorithm, through the error covariance matrix of computing, its a matter of fact so that the matrix transformation algorithm can be simplified, Platform: |
Size: 1024 |
Author:张艺谋 |
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Description: 集合卡尔曼滤波(EnKF) 数据同化方法可以避免了EKF 中协方差演变方程预报过程中出现的计算不准确和关于协方差矩阵的大量数据的存储问题,最主要的是可以有效的控制估计误差方差的增长,改善预报的效果。-Ensemble Kalman Filter (EnKF) data assimilation methods can be avoided in the EKF covariance forecasting the evolution equation arising in the course of the calculation is not accurate and on the covariance matrix of a large amount of data storage problems, the most important and effective control can be estimated error variance of the growth, improvement in forecasting results. Platform: |
Size: 5144576 |
Author:胡军 |
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Description: 一种Kalman滤波系统误差及其协方差矩阵的半参数估计方法的研究文章-A Kalman filter system error covariance matrix and the semi-parametric estimation methods of research papers Platform: |
Size: 354304 |
Author:王华 |
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Description: 利用误差协方差阵的迹最小准则建立了多传感器异步融合模型-Error covariance matrix using the minimum criteria for the establishment of a multi-track asynchronous sensor fusion model Platform: |
Size: 312320 |
Author:陈聪 |
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Description: 这是模式识别中最小错误率Bayes分类器设计方案。
自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。
调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。
主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。
-This is the minimum error rate pattern recognition Bayes classifier design.
Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array .
All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions .
Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function .
Call the minimum error rate decision Functions Bayesian Platform: |
Size: 4096 |
Author:崔杉 |
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Description: 这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。
调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。
主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。
-This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array .
All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions .
Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function .
Bayesian classifier Platform: |
Size: 4096 |
Author:崔杉 |
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Description: 目标运动和卡尔曼跟踪的仿真程序，给出了系统状态转移矩阵和测量过程，以及协方差和增益。通过绘图得出仿真轨迹和真实轨迹的平均误差。有助于研究目标航迹跟踪-Target motion and Kalman tracking simulation program, the system state transition matrix and measurement process, as well as the covariance and gain. Obtained by drawing the average error of the simulation trajectory and real trajectory. Help researchers target trajectory tracking Platform: |
Size: 6144 |
Author:周严 |
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Description: 协同定位是多平台编队中的关键问题之一 是实现无人机~ 舰艇编队等定位控制的基础G 从信息融
合的角度研究了编队协同中的导航定位问题 提出了一种新的协同定位算法G 推导了二维情况下 基于最近邻准
则确定伪测量和相伴误差协方差矩阵的模型G 仿真分析表明 该算法可以稳定地完成己平台运动要素的估计-Co-location is one of the key issues in the formation of multi-platform UAV ~ vessel formation positioning control G from the financial information
Together the perspective in the formation of collaborative navigation and positioning a new co-location algorithm G is derived adjacent quasi-two-dimensional case based on recent
Determine the pseudo-measurements and accompanied by the error covariance matrix of the model G simulation results show that the algorithm can steadily own platform motion elements estimated
Platform: |
Size: 152576 |
Author:dingwei |
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Description: 针对全球定位系统( GPS) 的微弱信号跟踪容易失锁问题, 提出一种新的跟踪方法. 利用平方根卡尔曼滤波算法, 在运算过程中运用误差协方差矩阵的平方根形式进行迭代, 有效减小了舍入误差带来的影响. 提出了一种新的GPS 信号跟踪测量模型, 在连续积分的基础上, 加入了非连续积分过程, 通过有效处理, 避免了导航信息位翻转对信号相关运算产生的影响-Weak signal tracking for the Global Positioning System (GPS), easy to loss of lock problem, propose a new tracking method using the square root of the Kalman filter algorithm, iterating use during operations in the form of the square root of the error covariance matrix, effectively reduced impact of rounding error. proposed a new GPS signal tracking measurement model based on continuous integral non-continuous integration process, through effective treatment and avoid navigation information bit flip signal operator affect Platform: |
Size: 331776 |
Author:yaomeng |
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Description: 大地测量相关观测抗差估计理论
相关观测异常诊断、质量控制是测量数据处理领域亟待解决的难题之一。分别从方差
膨胀模型和相关权元素压缩模型入手研究了相关观测的质量控制理论和方法；给出了误差影
响函数；构造了方差膨胀函数和权因子收缩函数；利用观测量的等价协方差阵和等价权矩阵讨
论了相关观测质量控制的计算方法。-The geodetic observations Robust estimated theoretical observing abnormal diagnostic quality control measure one of the problems solved in the field of data processing. Control theory and methods of compression model start to study the quality of the observations, respectively, from the elements of the variance inflation model and related rights given error influence function construct the functions and powers variance inflation factor contraction function utilize concept of equivalent covariance matrix of the measurement related observation quality control calculation method and the equivalent weight matrix. Platform: |
Size: 220160 |
Author:长沙 |
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Description: 探测三维冲突概率算法，通过对误差协方差矩阵的运算，对其进行一事实上的矩阵变换使算法进行简化，-Three-dimensional probabilistic conflict detection algorithm, through the error covariance matrix of computing, its a matter of fact so that the matrix transformation algorithm can be simplified, Platform: |
Size: 2048 |
Author:pinga7naoaa |
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Description: 现有的向量加权稳健波束形成方法只有在指向误差较小的情况下才能有效估计目标的信号功率；矩阵加权波束形成方法在指向误差较大时，虽然可以估计目标的信号功率，但是它的系统实现复杂度与向量加权稳健波束形
成方法相比较大。针对以上问题，该文提出基于半正定秩松弛(SDR)方法的稳健波束形成，该方法优化模型中的目标函数与Capon 算法的目标函数相同，优化变量为加权向量的协方差矩阵，并约束方向图的主瓣幅度波动范围、旁瓣电平，协方差矩阵的秩为1。-The existing vector weighted robust beamforming is able to estimate the signal power of target only in
situations of a small steering angle error. For a larger steering angle error case, although the matrix weighted
beamforming can effectively estimate the signal power of the target as well, the system implementation is more
complicated than above mentioned vector weighted. In order to solve these problems, this paper presents a new
robust beamforming approach based on SemiDefinite rank Relaxation (SDR). Detailed description of the proposed
method are given as follows: the optimal model has the same objective as that of the Capon algorithm the
optimization variable is the covariance matrix of weight vector with constraints posed on the ripple of mainlobe
amplitude and sidelobe level, and the rank of covariance matrix is 1. Platform: |
Size: 688128 |
Author:treedev |
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Description: 采用遗传算法对 EKF 中的系统噪声矩阵和测量矩阵的协方差进行在线优化，以实现在模型误差最小时对 SOC 进行在线估计(Genetic algorithm is used to optimize the covariance of system noise matrix and measurement matrix in EKF on-line, so as to realize the on-line estimation of SOC at the minimum model error) Platform: |
Size: 248832 |
Author:中国足球队 |
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Description: a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。
（b）在上述数据集上和分别属于+1类和-1类，请在上述数据集的两类中各随机抽取150个样本作为训练集，运用Logistic regression算法得到的分类面，然后对余下的各50个样本进行分类，画出测试样本及其分类面，统计错误率，给出每个样本属于该类别的概率值。(a) Generate the sum of two datasets with 200 two-dimensional vectors (Note: before generating the dataset, it is better to initialize the Gaussian random generator to 0 (or any given value) with the command randn ("seed", 0), which is important for the repeatability of the results). The first half of the vector comes from the normal distribution of the mean vector and the covariance matrix. The second half of the vector comes from the normal distribution of the mean vector and the covariance matrix. Where is a 2 * 2 identity matrix.
(b) On the above datasets, and belong to + 1 and - 1 classes respectively. Please randomly select 150 samples from each of the above data sets as the training set, use the logistic regression algorithm to get the classification surface, and then classify the remaining 50 samples, draw the test samples and their classification surface, count the error rate, and give the probability value of each sample belonging to this category.) Platform: |
Size: 1024 |
Author:zilong1999 |
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Search - error covariance matrix