State Estimation
State estimation aims to accurately determine the current state of a system—be it a robot, vehicle, or other dynamic entity—by fusing data from various sensors and incorporating models of system dynamics. Current research emphasizes robust and efficient algorithms, such as Kalman filters (including variations like Invariant Extended Kalman Filters and Moving Horizon Estimation), particle filters, and neural network-based approaches, to handle nonlinearity, sensor noise, and uncertainties in complex environments. These advancements are crucial for improving the performance and reliability of autonomous systems in diverse applications, ranging from robotics and autonomous driving to marine navigation and multi-agent coordination.
164papers
Papers
March 11, 2025
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment
March 7, 2025
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February 28, 2025
January 27, 2025