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.


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    Title :

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


    Additional title:

    Advances in Computer Science res


    Contributors:

    Conference:

    International Conference on Image, Algorithms and Artificial Intelligence ; 2024 ; Singapore, Singapore August 09, 2024 - August 11, 2024



    Publication date :

    2024-10-13


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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