基于卡尔曼滤波的地下水动态数据的预处理及JAVA实现

发布时间：2018-01-15 11:51

本文关键词：基于卡尔曼滤波的地下水动态数据的预处理及JAVA实现　出处：《内蒙古农业大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着计算机通信以及数据库、传感器等现代技术的高速发展,当今社会正高速的向信息化社会跨进。而随着数字化信息的实现,信息融合也悄然成为各研究学科的新宠,普遍的应用在水资源数据分析等诸多领域。在对地下水资源进行监测的过程中,难免会受到诸多条件和因素的限制,导致量测到的数据或多或少的存在着一定的偏差。为了能够精准的对地下水状态数据进行掌控,根据地下水状态估计问题,本文进行了基于卡尔曼滤波(KF)的地下水数据处理研究。主要工作内容如下:设计并完成了基于卡尔曼滤波算法的地下水信息的数学估计模型,并结合实际测量数据的计算,给出了模型的各参数值大小。运用递推的方式,通过一系列公式的推导,最终得到地下水信息的精确估计。在MATLAB软件中,对估计前后的数据进行比较检验,显示结果表明该算法能够较优秀的实现对地下水数据的融合滤波处理,数据的精度得以提高。最后,结合数据库和相应开发语言的支持,实现了对地下水动态数据信息的相应可视化展示。
[Abstract]:With the rapid development of computer communication and database, sensors and other modern technology, today's society is high into the information society. With the realization of digital information, information fusion has quietly become the subject of new, widely used in many fields such as water resources data analysis. In the process of monitoring of groundwater resources. There are many conditions and factors leading to measured data more or less exist certain deviation. In order to accurately control the groundwater state data, according to the state estimation problem of underground water, this paper based on Calman filter (KF) of groundwater data processing research. The main contents are as follows: design and the estimation model based on groundwater information Calman filtering algorithm of the mathematics, and combined with the calculation of actual measurement data, given the model The parameter values of the size. The use of recursive way, by deriving a series of formulas, finally get the accurate estimation of groundwater information. In MATLAB software, to estimate and compare the data before and after the test, display the results show that the proposed algorithm can achieve better fusion of groundwater data filtering, data accuracy improved. Finally, with the combination of database and the corresponding programming language support, the realization of the corresponding groundwater dynamic data information visualization.

【学位授予单位】：内蒙古农业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN713;P641

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