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
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14 Mar 2010TL;DR: The resulting algorithms are robust to node and link failure, scalable, and fully distributed, in the sense that no fusion center is required, and nodes communicate with their neighbors only.
Abstract: We study the problem of distributed state-space estimation, where a set of nodes are required to estimate the state of a nonlinear state-space system based on their observations. We extend our previous work on distributed Kalman filtering to the nonlinear case, and propose algorithms for Extended and Unscented Kalman filtering. The resulting algorithms are robust to node and link failure, scalable, and fully distributed, in the sense that no fusion center is required, and nodes communicate with their neighbors only. We apply the algorithms to the problem of estimating the position of every node in an ad-hoc network, also known as wireless localization. Simulation results illustrate the performance of the proposed algorithms.
55 citations
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TL;DR: In this paper, the authors further explore the capability of EnKF, focusing on some practical issues including the correction of the linear and Gaussian assumptions during filter updating with iteration, the reduction of ensemble size with a resampling scheme, and the impact of data assimilation time interval.
Abstract: The concept of “closed-loop” reservoir management is currently receiving considerable attention in the petroleum industry. A “realtime” or “continuous” reservoir model updating technique is a critical component for the feasible application of any closed-loop, model-based reservoir management process. This technique should be able to rapidly and continuously update reservoir models assimilating the up-to-date observations of production data so that the performance predictions and the associated uncertainty are up-to-date for optimization of future development/operations. The ensemble Kalman filter (EnKF) method has been shown to be quite efficient for this purpose in large-scale nonlinear systems. Previous studies show that a relatively large ensemble size is required for EnKF to reliably assess the uncertainty, and a confirming step is recommended to ensure the consistency of the updated static and dynamic variables with the flow equations. In this paper, we further explore the capability of EnKF, focusing on some practical issues including the correction of the linear and Gaussian assumptions during filter updating with iteration, the reduction of ensemble size with a resampling scheme, and the impact of data assimilation time interval. Results from the example in this paper demonstrate that the proposed iterative EnKF performs better with more accurate predictions and less uncertainty than the traditional noniterative EnKF. The use of iteration reduces the impact of nonlinearity and non-Gaussianity. Results also show that iteration may only be required when predictions are considerably deviated from the observations. The proposed resampling scheme can significantly reduce the ensemble size necessary for reliable assessment of uncertainty with improved accuracy. Finally, we show that the noniterative EnKF is sensitive to the size of time interval between the assimilation steps. Using the proposed iterative EnKF, results are more stable, more accurate reservoir models and predictions can be obtained even when a large time interval is used. This also indicates that iteration within the EnKF updating serves as a process that corrects the stronger nonlinear and non-Gaussian behaviors when larger time interval is used.
55 citations
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TL;DR: In this paper, a robust three-step unscented Kalman filter is proposed to reduce the sensitivity to the initial condition error when dealing with air data sensor fault estimation, which can cope with not only the estimation of the air sensor faults, but also the detection and isolation of faults.
Abstract: Air data sensor fault detection and diagnosis is important for the safety of aircraft. In this paper, first an extension of the robust three-step Kalman filter to nonlinear systems is made by proposing a robust three-step unscented Kalman filter. The robust three-step unscented Kalman filter is found to be sensitive to the initial condition error when dealing with air data sensor fault estimation. A theoretical analysis of this sensitivity is presented and a novel adaptive three-step unscented Kalman filter is proposed which is able to cope with not only the estimation of the air data sensor faults, but also the detection and isolation of faults. The adaptive three-step unscented Kalman filter contains three steps: time update, fault estimation and measurement update. This approach can reduce the sensitivity to the initial condition error. Finally, the air data sensor fault detection and diagnosis performance of the adaptive three-step unscented Kalman filter is validated using simulated aircraft data. Ad...
54 citations
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TL;DR: In this article, Luenberger's minimal-order observer is considered as an alternate to the Kalman filter for obtaining state estimates in linear discrete-time stochastic systems and the observer solution is extended to systems for which the noise disturbances are time-wise correlated processes of the Markov type.
54 citations
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TL;DR: In this paper, the adaptive fading extended Kalman filter (AFEKF) is analyzed and the stability of the filter is analyzed based on the analysis result of Reif and co-authors for the EKF.
Abstract: The well-known conventional Kalman filter gives the optimal solution but to do so, it requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system model and the stochastic information are incomplete. The Kalman filter with incomplete information may be degraded or even diverged. To solve this problem, a new adaptive fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper bounded condition of the error covariance for the filter stability and the bounded value of the estimation error. Keywords: Adaptive Kalman filter, forgetting factor, nonlinear filter, stability analysis.
54 citations