Topic
Alpha beta filter
About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this paper, the dynamic data reconciliation (DDRF) filter is proposed to deal with both white and autocorrelated noise in a binary distillation column with four controlled variables.
55 citations
02 Aug 2010
TL;DR: In this article, the authors analyze the applicability of three types of consider Kalman fllters on a simple asteroid rendezvous scenario and show that a Minimum Variance Consider Kalman Filter can provide not only improved state estimates to a traditional Kalman filter, but also produces consistent results from a statistical perspective.
Abstract: Parameter errors in dynamic and measurement models of dynamic systems can result in poor state estimates when using a traditional Kalman fllter structure. In dealing with these parameter errors it is possible to: 1) Ignore them completely; 2) Add the parameters as additional states to be estimated; or 3) \Consider" the error in the state covariance matrix by introducing additional parameter covariance matrices. This paper analyzes the efiect of using all three of these types of fllters on a simple asteroid rendezvous scenario to determine the applicability of each. Two types of consider Kalman fllters are explored, namely an Augmented Measurement Consider Kalman Filter and a Minimum Variance Consider Kalman Filter. This paper flnds that a Minimum Variance Consider Kalman Filter can provide not only improved state estimates to a traditional Kalman fllter, but also produces consistent results from a statistical perspective.
55 citations
TL;DR: In this paper, the authors proposed to incorporate a neural network into the normal Kalman filter configuration such that the neural network provides the adaptive capability the filter needs, thus reducing the estimation error.
Abstract: Kalman filtering is a fundamental building block of most multiple-target tracking (MTT) algorithms. The other building block usually involves some type of data association schemes. Here it is proposed to incorporate a neural network into the normal Kalman filter configuration such that the neural network provides the adaptive capability the filter needs. As such the estimation error of the Kalman filter would be reduced, hence improving the MTT solution. Simulation results have shown that this claim is valid. >
55 citations
TL;DR: Appropriate analysis of the observer output is shown to yield estimates of the faults with no false or missed detections and a recently proposed ship propulsion benchmark problem is considered.
55 citations
09 Oct 2006
TL;DR: Three extensions to the Kalman filter algorithm are presented: extendedKalman filter (EKF), limiting EKF (LimEKf), and unscented Kalman filters (UKF).
Abstract: The problem of on-line calibration of dynamic traffic assignment (DTA) models is receiving increasing attention from researchers and practitioners. The problem can be formulated as a non-linear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter and therefore non-linear extensions need to be considered. In this paper, three extensions to the Kalman filter algorithm are presented: extended Kalman filter (EKF), limiting EKF (LimEKF), and unscented Kalman filter (UKF). The solution algorithms are applied to the calibration of the state-of-the-art DynaMIT-R DTA model and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy comparable to that of the best algorithm, but vastly superior computational performance
55 citations