Kd Ekf

Kd-EKF, or Kalman Decomposition Extended Kalman Filter, represents a family of algorithms addressing the limitations of standard Extended Kalman Filters (EKFs) in various applications, primarily focusing on improving consistency and accuracy of state estimation in complex systems. Current research emphasizes enhancements to EKF through techniques like invariant filtering on Lie groups, observability constraints, and Kalman decomposition to handle nonlinear dynamics and noisy sensor data, particularly in robotics and navigation. These advancements lead to more robust and reliable state estimation in challenging environments, impacting fields like autonomous driving, drone navigation, and human motion capture by providing more accurate and consistent localization and tracking.