Adaptive Kalman

Adaptive Kalman filtering enhances the robustness of Kalman filters by dynamically adjusting parameters, primarily the process noise covariance, to account for variations in system dynamics and sensor noise. Current research focuses on integrating adaptive Kalman filters with various machine learning techniques, such as neural networks and reinforcement learning algorithms, to improve accuracy and adaptability in diverse applications. This approach is proving valuable in numerous fields, including autonomous navigation (e.g., vehicles, underwater vehicles, spacecraft), human motion analysis, and multi-agent systems, by enabling more accurate state estimation and improved decision-making in uncertain environments.