An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
When the multisensor self-adaptive weighted fusion algorithm fuses the data sources that were severely interfered by noise, its fusion precision, data smoothness, and algorithm stability will be reduced.
To overcome this drawback, the idea was proposed with respect to an improved algorithm which optimized acquisition of fusion data sources with discrete Kalman filtering technique, thus reducing the negative impact on the fusion performance from noise.
To verify the effectiveness of the improved algorithm, this paper simulated the fusion process of soil moisture data with fusion samples.
The result proved that, under the same circumstance, the improved algorithm has a stronger restrain ability to noise and a better performance in fusion precision, data smoothness, and algorithm stability compared with the general algorithm.
American Psychological Association (APA)
Shao, Shifen& Zhang, Kaisheng. 2020. An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering. Complexity،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1145743
Modern Language Association (MLA)
Shao, Shifen& Zhang, Kaisheng. An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering. Complexity No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1145743
American Medical Association (AMA)
Shao, Shifen& Zhang, Kaisheng. An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering. Complexity. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1145743
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1145743