Data Fusion in UAV Sensors using Kalman Filter Algorithm and Fuzzy Algorithm

DOI

10.2991/978-94-6463-540-9_41How to use a DOI?

Keywords

Multi-sensor; Fusion Algorithms; Data Fusion; Kalman Filter

Abstract

The increasing use of Unmanned Aerial Vehicles (UAVs) in various sectors like agriculture, surveying, logistics, and environmental monitoring has created a pressing need for the ability to gather and process positioning sensor data. The precision of positioning, equipment performance, and data processing efficiency are critical factors that influence the successful completion of UAV missions. However, there is a lack of sufficient research in this vital area. This paper aims to explore the data fusion techniques based on the Kalman Filter Algorithm and Fuzzy Algorithm in UAV sensors. The objective is to understand how these methods can enhance the accuracy and reliability of UAV operations. The paper first introduces the application of the Kalman Filter Algorithm in data fusion. Next, the paper explains the role of the Fuzzy Algorithm in handling the uncertainty of sensor data. The paper then states the effectiveness and reliability of the data fusion techniques based on the Kalman Filter Algorithm and Fuzzy Algorithm in UAV sensors. In conclusion, the data fusion techniques based on the Kalman Filter Algorithm and Fuzzy Algorithm can be instrumental in enhancing the performance of UAVs. The significance of this research lies in its potential to contribute to the advancement of UAV technology, thereby benefiting various industries that rely on UAVs.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tianchen Huang
PY  - 2024
DA  - 2024/10/16
TI  - Data Fusion in UAV Sensors using Kalman Filter Algorithm and Fuzzy Algorithm
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 413
EP  - 422
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_41
DO  - 10.2991/978-94-6463-540-9_41
ID  - Huang2024
ER  -