From July 7-11, 2024, IFT attended and presented a paper at the 27th International Conference on Information Fusion in Venice, Italy. The paper, titled “RNN-UKF: Enhancing Hyperparameter Auto-Tuning in Unscented Kalman Filters through Recurrent Neural Networks,” was authored by a team from IFT, including Fan, Zhengyang; Shen, Dan; Bao, Yajie; Pham, Khanh; Blasch, Erik; and Chen, Genshe. The presentation focused on integrating Recurrent Neural Networks (RNNs) with Unscented Kalman Filters (UKFs) to improve the efficiency and accuracy of hyperparameter tuning, a critical aspect of filtering and estimation in complex systems. IFT’s participation in this prestigious conference highlights our commitment to advancing research in information fusion and contributing to the global academic community. The insights and feedback received during the conference will further inform our research and development efforts as we continue to push the boundaries of what’s possible in data fusion technologies. For more information about the conference, you can visit the official event page here.

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