Author(s)

Haihong Jiang ¹

Affiliation(s)

¹ Heilongjiang University of Finance and Economics, Harbin, 150030, China

Corresponding Author

Haihong Jiang

ABSTRACT

Teaching Model is the oldest and most far-reaching theoretical work in the world. It contains many unique insights, is full of philosophical thinking, and is still of great significance in modern life. Therefore, until now, many people are still exploring the ideas in it. There are many esoteric theories in teaching mode that are difficult to understand in a traditional way, so they need to be explained with the help of other methods. As a special data processing method, multi-data fusion has received great attention and development in the field of target recognition. This text focused on the teaching mode of teaching mode on account of multi-data fusion algorithm and virtual simulation technology (VST). This paper interprets its profound theories with the help of data fusion and VSTs in a pedagogical mode to enhance students' understanding and interest in the course. In this paper, a data fusion estimation model based on Kalman filtering was established, simulation experiments were carried out on data of different structures, and the estimation accuracy of the system was improved through multi-sensor data fusion. The simulation virtual system was introduced in the teaching, and the role of the virtual simulation system was explored in the teaching process by comparing teaching between parallel classes and experimental classes. The experimental results of this paper showed that, from the perspective of traditional teaching methods, the ability level between 40-45 was 13.6%, and between 45-50 was 4.2%. In the virtual simulation teaching mode, the ability level was 6% between 45-50, and 15% between 40-45. 

KEYWORDS

Teaching Model; Virtual Simulation; Data Fusion; Traditional Teaching

CITE THIS PAPER

Haihong Jiang, Teaching Mode Based on Multi-Data Fusion Algorithm and Virtual Simulation Technology. Automation and Machine Learning (2024) Vol. 5: 59-71. DOI: http://dx.doi.org/10.23977/autml.2024.050207.

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