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