电子档案信息化数据自适应无迹卡尔曼滤波降噪算法

周婷

doi:10.11959/j.issn.1000-0801.2023151

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电子档案信息化数据自适应无迹卡尔曼滤波降噪算法

研究与开发 | 更新时间：2024-06-05

- 电子档案信息化数据自适应无迹卡尔曼滤波降噪算法
- An adaptive unscented Kalman filter algorithm for electronic archives information data denoising
- 电信科学 2023年39卷第8期页码：102-108
- 作者机构：
- 作者简介：
  
  [ "周婷（1990- ），女，国网山西省电力公司营销服务中心工程师，主要研究方向为电力客户服务、电力市场等" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023151
  中图分类号： TP391.9
- 网络出版日期：2023-08，
  
  纸质出版日期：2023-08-20
- 稿件说明：
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周婷. 电子档案信息化数据自适应无迹卡尔曼滤波降噪算法[J]. 电信科学, 2023,39(8):102-108.

Ting ZHOU. An adaptive unscented Kalman filter algorithm for electronic archives information data denoising[J]. Telecommunications science, 2023, 39(8): 102-108.
周婷. 电子档案信息化数据自适应无迹卡尔曼滤波降噪算法[J]. 电信科学, 2023,39(8):102-108. DOI： 10.11959/j.issn.1000-0801.2023151.

Ting ZHOU. An adaptive unscented Kalman filter algorithm for electronic archives information data denoising[J]. Telecommunications science, 2023, 39(8): 102-108. DOI： 10.11959/j.issn.1000-0801.2023151.

摘要

针对降噪过程极易丢失原始数据，产生粗大误差后数据的噪声协方差初始值偏差的问题，研究了电子档案信息化数据自适应无迹卡尔曼（Kalman）滤波降噪算法。电子档案信息化架构包含数据层、业务层、用户层，数据层根据用户层的用户数据请求，完成电子档案信息化数据预处理、决策、监测、分析等，通过拉以达（Laida）准则对电子档案信息化数据提出假设，获取标准偏差概率，确定区间剔除粗大误差，应用Sage-Husa滤波器估计剔除粗大误差后数据的噪声协方差、抑制初始值偏差，最大限度地保留其原始数据，利用无迹卡尔曼算法，实时估计电子档案信息化数据的未知噪声特性，完成电子档案信息化数据降噪，并通过虚拟感应服务连接数据层、用户层、业务层，在业务层呈现用户所需电子档案信息。实验结果表明，该算法能够有效去除电子档案信息化数据的多种噪声，并保留有效数据。

Abstract

The adaptive unscented Kalman filter noise reduction algorithm for electronic archives information data was studied to address the issue of data loss during the noise reduction process

as well as the noise covariance and initial value deviation of the data after gross errors.The architecture of electronic archives informatization consisted of the data

business

and user layers.In the data layer

the electronic archives informatization data underwent pretreatment

decision-making

monitoring

and analysis based on user data requests from the user layer.Assumptions were made on the electronic archives informatization data using the Laida criterion to determine the standard deviation probability and establish intervals.Gross errors were eliminated and the noise covariance of the data after gross error removal was estimated using the Sage-Husa filter.This helped to suppress the deviation of the initial value and preserve the original data as much as possible.The traceless Kalman algorithm was utilized to estimate the unknown noise characteristics of electronic archives informatization data in real-time

enabling the noise reduction of electronic archives informatization data.The virtual induction service connected the data

users

and business layer

facilitating the presentation of the required electronic archives information to users in the business layer.Experimental results demonstrate that the algorithm effectively removes various noises from electronic archives information data while retaining the valid data.

关键词

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references

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