2018/08/28 by Torben Knudsen, John Leth · 46 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Alpha beta filter #Artificial intelligence #Computer science #Computer vision #Control theory (sociology) #Discrete time and continuous time #Ensemble Kalman filter #Extended Kalman filter #Fast Kalman filter #Filter (signal processing) #Filter design #Inertial Sensor and Navigation #Invariant extended Kalman filter #Kalman filter #Mathematics #Moving horizon estimation #Nonlinear filter #Nonlinear system #Physics #Scientific Research and Discoveries #Statistics #Target Tracking and Data Fusion in Sensor Networks #Unscented transform
paper · open access · doi:10.1109/tac.2018.2867325
published in IEEE Transactions on Automatic Control 64(5), 2198-2205 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2018/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
The time and measurement update for the discrete time Kalman filter can be formulated in terms of conditional means and covariances. The unscented Kalman filter can be interpreted as calculating these conditional means and covariances by using the unscented transform. This approach can also be directly applied to nonlinear models as an alternative to the discrete time extended Kalman filter. In this paper, a novel method for computing the unscented Kalman filter for a nonlinear model with continuous time dynamics and discrete time measurements is presented. Compared to the existing approaches, this method is far simpler and less computationally demanding, and it performs at least as well.