电子档案信息化数据自适应无迹卡尔曼滤波降噪算法

doi:10.11959/j.issn.1000-0801.2023151

摘要/Abstract

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。

关键词: 电子档案, 无迹卡尔曼, 档案信息化, 滤波降噪, Sage-Husa滤波器, 拉以达准则

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.

Key words: electronic archives, unscented Kalman, archives informatization, filtering and noise reduction, Sage-Husa filter, Laida criterion

中图分类号:

TP391.9

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.

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信号/时间	9:00	10:00	11:00	12:00	13:00	14:00
航向	35°	42°	54°	32°	47°	58°
风向	31°	56°	48°	25°	48°	56°
航速/knots	34	51	50	34	51	53
高度/ft	37	46	47	29	54	61
空速/knots	35	50	49	32	44	58
姿态	33°	49°	51°	31°	56°	62°
气压/inHg	30	53	46	35	52	61
乘性噪声/dB	35	42	50	32	54	46
白噪声/dB	31	40	42	26	51	57
色噪声/dB	24	38	40	25	53	56
加性噪声/dB	31	42	—	28	—	42
高斯白噪声/dB	28	—	12	—	—	23

信号/时间	9:00	10:00	11:00	12:00	13:00	14:00
航向	35°	42°	54°	32°	47°	58°
风向	31°	56°	48°	25°	48°	56°
航速/knots	34	51	50	34	51	53
高度/ft	37	46	47	29	54	61
空速/knots	35	50	49	32	44	58
姿态	33°	49°	51°	31°	56°	62°
气压/inHg	30	53	46	35	52	61
乘性噪声/dB	—	—	—	—	—	—
白噪声/dB	—	—	—	—	—	—
色噪声/dB	—	—	—	—	—	—
加性噪声/dB	—	—	—	—	—	—
高斯白噪声/dB	—	—	—	—	—	—

信号数据		信噪比/dB
信号数据	本文算法	文献[4]算法	文献[5]算法
航向	63	32	49
风向	67	31	45
航速	65	42	42
高度	63	37	48
空速	61	38	44
姿态	66	34	49
气压	64	36	45