Observability Analysis

Observability analysis focuses on determining what aspects of a system's state can be reliably inferred from available measurements, a crucial problem across diverse fields. Current research emphasizes developing robust algorithms and models, such as extended Kalman filters, optimized transformers (like Toto), and novel approaches leveraging Lie groups and preintegration, to address challenges in various applications including robotics, sensor fusion, and time series forecasting. These advancements improve the accuracy and consistency of state estimation, leading to better control, decision-making, and ultimately, more reliable autonomous systems and data-driven insights.