Fusion Algorithm

Fusion algorithms combine data from multiple sources to improve accuracy and robustness in various applications, ranging from generative modeling and autonomous driving to indoor localization and anomaly detection. Current research focuses on developing efficient and robust fusion methods, employing architectures like transformers, Kalman filters (including variations like SRUKF), and deep learning models (e.g., CNN-LSTM combinations) to address challenges such as noisy data, non-line-of-sight issues, and the need for generalization across diverse datasets. These advancements are significantly impacting fields requiring reliable data integration, leading to improvements in areas like robotic perception, medical imaging, and industrial monitoring.