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    • Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup

    James S. Wright,Mengwei Sun,Mike E. Davies,Ian K. Proudler,James R. Hopgood

    2024 27TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, FUSION 2024（2024）

    Dstl | Cranfield Univ | Univ Edinburgh

    Cited 0|Views2
    Abstract
    This paper explores the increasing demand for accurate and resilient multi-sensor fusion techniques, particularly within 3D tracking systems enhanced by drone technology. Employing the adaptive kernel Kalman filter (AKKF) methodology within the Stone Soup framework, our research seeks to develop robust fusion approaches capable of seamlessly amalgamating data from a multi-sensor arrangement with fixed ground sensors and dynamic sensors mounted on drones. By capitalising on the adaptive nature of the $A K K F$, we aim to refine the precision and dependability of 3D object tracking in intricate scenarios. Through empirical evaluations, we illustrate the effectiveness of our proposed AKKF-based fusion strategies in enhancing tracking performance within the Stone Soup framework, thus contributing to the advancement of multi-sensor fusion methodologies within this framework.
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    Adaptive kernel Kalman filter,sensor fusion, Stone Soup,3D Tracking
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    【要点】：本文提出在Stone Soup框架下，利用自适应核卡尔曼滤波（AKKF）方法，实现了固定地面传感器与无人机动态传感器的多传感器数据融合，以提高3D跟踪系统的准确性和鲁棒性。
    

    【方法】：研究采用自适应核卡尔曼滤波（AKKF）技术，利用其自适应特性优化多传感器数据融合过程，增强3D对象跟踪的精度和可靠性。
    

    【实验】：通过实证评估，本文展示了所提出的AKKF-based融合策略在Stone Soup框架内提升跟踪性能的有效性，但论文中未明确提及所使用的数据集名称及具体实验结果。