scispace - formally typeset
Search or ask a question
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
More filters
Book ChapterDOI
15 Apr 1996
TL;DR: A novel technique for the automatic adaptation of a deformable model's elastic parameters within a Kalman filter frame-work for shape estimation applications by augmenting the state equations of an extendedKalman filter to incorporate these additional variables and take into account the noise in the data.
Abstract: We present a novel technique for the automatic adaptation of a deformable model's elastic parameters within a Kalman filter frame-work for shape estimation applications. The novelty of the technique is that the model's elastic parameters are not constant, but time varying. The model for the elastic parameter variation depends on the local error of fit and the rate of change of the error of fit. By augmenting the state equations of an extended Kalman filter to incorporate these additional variables and take into account the noise in the data, we are able to significantly improve the quality of the shape estimation. Therefore, the model's elastic parameters are initialized always to the same value and they subsequently modified depending on the data and the noise distribution. In addition, we demonstrate how this technique can be parallelized in order to increase its efficiency. We present several experiments to demonstrate the effectiveness of our method.

52 citations

Journal ArticleDOI
TL;DR: An extended Kalman filter (EKF), implementing the full nonlinear kinematics of the aircraft equations of motion, was used for the estimation of aerodynamic coefficients in aircraft dynamic models from flight-test data.
Abstract: The estimation of aerodynamic coefficients in aircraft dynamic models from flight-test data is addressed in this paper. An extended Kalman filter (EKF), implementing the full nonlinear kinematics of the aircraft equations of motion, was used for this purpose. Flight-test data from NASA's X-31 Drop Model and High Angle-of-attack Research Vehicle (HARV) were analyzed. The EKF parameter estimates for the X-31 compared well with wind-tunnel data and flight-data results using other identification techniques. For the HARV, the assumption of pseudonoise in the parameter dynamic model substantially improved the state and parameter estimates. A residual correlation method was used to estimate the process noise intensity matrix for this aircraft's flight data.

52 citations

Journal ArticleDOI
TL;DR: In this article, a new observer that estimates the exact state of a linear continuous-time system in predetermined finite time is presented, which is achieved by updating the observer state based on the difference between the measured output and the estimated output at discrete time instants.

52 citations

Journal ArticleDOI
TL;DR: It is shown that there exists a local state observer for a nonlinear system if it has robust relative degree n, and the proposed observer utilizes the coordinate change which transforms a system into an approximate normal form.
Abstract: In this paper, we present a state observer for single-input/single-output nonlinear systems which fail to have well defined relative degree. It is shown that there exists a local state observer for a nonlinear system if it has robust relative degree n. The proposed observer utilizes the coordinate change which transforms a system into an approximate normal form. The proposed method is applied to a ball and beam system, and simulation results show that substantial improvement in the performance was achieved compared with other local observers.

52 citations

Journal ArticleDOI
TL;DR: This paper presents the optimal two-stage Kalman filter for systems that involve noise-free observations and constant but unknown bias, which provides an alternative to state vector augmentation and offers the same potential for improved numerical accuracy and reduced computational burden.
Abstract: This paper presents the optimal two-stage Kalman filter for systems that involve noise-free observations and constant but unknown bias. Like the full-order separate-bias Kalman filter, this new filter provides an alternative to state vector augmentation and offers the same potential for improved numerical accuracy and reduced computational burden. When dealing with systems involving accurate, essentially noise-free measurements, this new filter offers an additional advantage, a reduction in filter order. The optimal separate-bias reduced order estimator involves a reduced order filter for estimating the state, the order equalling the number of states less the number of observations.

52 citations


Network Information
Related Topics (5)
Control theory
299.6K papers, 3.1M citations
90% related
Robustness (computer science)
94.7K papers, 1.6M citations
88% related
Control system
129K papers, 1.5M citations
87% related
Optimization problem
96.4K papers, 2.1M citations
83% related
Nonlinear system
208.1K papers, 4M citations
80% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202331
202277
20211
201910
201836
2017269