UKF

(redirected from Unscented Kalman filter)
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AcronymDefinition
UKFUnscented Kalman Filter
UKFUniversal-Kugellager-Fabrik (German: Universal Ball-Bearing Factory; Berlin, Germany)
UKFUniversity Kickboxing Federation (Japan)
UKFUniverzita Konštantína Filozofa (Slovakia)
UKFUmeed Khanna Foundation (est. 1996; India)
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References in periodicals archive ?
Yang, "Stochastic stability of a modified unscented Kalman filter with stochastic nonlinearities and multiple fading measurements," Journal of the Franklin Institute, vol.
Particle filter shows better accuracy than Extended and Unscented Kalman Filter, because it close to Bayesian recursive filter estimation when enough sample data exist.
To overcome the shortcomings of traditional adaptive filter, we developed a new nonlinear adaptive square root unscented Kalman filter (NASRUKF) which can be used in nonlinear or linear system for multisensory data fusion with uncertain process noise [15, 16].
Hawkeye King, and Blake Hannaford, "Robustness of the Unscented Kalman Filter for State and Parameter Estimation in an Elastic Transmission," In Proceedings of the Robotics: Science and Systems, 2009.
Lee, "Performance enhancement for ultra-tight GPS/INS integration using a fuzzy adaptive strong tracking unscented Kalman filter," Nonlinear Dynamics, vol.
Wan, "The square-root unscented Kalman filter for state and parameter-estimation," in Proceedings of the IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, pp.
Hajiyev, "Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults," ISA Transactions, vol.
Mu, "The fault-detection method of a navigation system based on a strong tracking unscented kalman filter," Journal of Harbin Engineering University, Vol.
Several solutions to this problem have been proposed including the EKF, which linearizes the nonlinear function by taking its first-order Taylor approximation, and the unscented Kalman filter (UKF), which approximates the probability density function (PDF) using a nonlinear transformation of the random variable.
This lemma has been used to approach the stability of the extended Kalman Filter (EKF) [23] and later for a general class of nonlinear filters including EKF and unscented Kalman filter (UKF) [24,25].
When computational power is sufficient, the Unscented Kalman filter or Particle filters can be used for estimation, but according to our experiences the improvement of localization is not substantial.
One such nonlinear filtering algorithm is the unscented Kalman filter (UKF) developed in Julier et al.