Download or read book Multiple Model Adaptive Estimation Using Filter Spawning written by Kenneth Fisher and published by . This book was released on 1998-03-01 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Model Adaptive Estimation with Filter Spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream . Filter Spawning is used to include additional filters with partial actuator failure hypotheses into the Multiple Model Adaptive Estimation (MMAE) bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuator-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis (or, an effectiveness of 100%), a complete actuator failure hypothesis (or, an effectiveness of 0%), and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared to what is possible without spawning additional filters, making partial failure adaptation a viable methodology in a manner heretofore unachieved.
Download or read book Multiple Model Adaptive Estimation Using Filter Spawning written by Kenneth A. Fisher (2LT, USAF.) and published by . This book was released on 1999 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiple Model Adaptive Estimation and Prediction with the Harmonically Balanced Kalman Filter Bank written by Randall Joseph Muravez and published by . This book was released on 1989 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Generalized Multiple Model Adaptive Estimation written by Badr N. Alsuwaidan and published by . This book was released on 2008 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: ?Pub Inc In this dissertation a generalized multiple-model adaptive estimator (GMMAE) is presented that can be used to estimate the unknown noise statistics in filter designs. The assumed unknowns in the adaptive estimator are the process noise covariance elements. Multiple parameter elements are used to drive multiple-model parallel filters for state estimation. The current approach focuses on estimating the process noise covariance by sequentially updating weights associated with parameter elements through the calculation of the likelihood function of the measurement-minusestimate residuals, which also incorporates correlations between various measurement times. For linear Gaussian measurement processes the likelihood function is easily determined. For nonlinear Gaussian measurement processes, it is assumed that the linearized output sufficiently captures the statistics of the likelihood function by making the small noise assumption. A proof is provided that shows the convergence properties of the generalized approach versus the traditional multiple-model adaptive estimator (MMAE). Simulation results, involving a two-dimensional target tracking problem ans GPS-based position estimation problem using an extended Kalman filter, indicate that the new approach is able to correctly estimate the noise statistics.
Download or read book New Variations of Multiple Model Adaptive Estimation for Improved Tracking and Identification written by Christopher K. Nebelecky and published by . This book was released on 2013 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple model adaptive estimation (MMAE) is a recursive algorithm that uses a bank of estimators, each purposefully dependent on a particular hypothesis, to determine an estimate of an uncertain system under consideration while simultaneously tracking the system state. The first generation of MMAE, introduced by Magill in 1965 considered the estimators to act independently and in parallel, determining state estimates conditional with each hypothesis. Through computation of a normalized mode-conditioned likelihood, the conditional probability that each hypothesis correctly models the system is computed. Since Magill's seminal work, many offshoots of MMAE have been developed. Modifications have been reported, but are typically on on an application specific basis which limits their versatility. In this dissertation, two variations of MMAE are considered. The first variation is based on an observed flaw which leads to degenerate tracking performance. The second variation is motivated by previous research which showed improved convergence performance by considering a generalized mode-conditioned likelihood function for determining the hypothesis conditional probabilities. Each estimator, or specifically Kalman filter, is designed around a particular system hypothesis. If the hypothesis is not sufficiently close to the true system, the resulting filter will generally produce erroneous estimates which do not track the system. This is because each filter believes that the hypothesized system is optimal. Further, the state error covariances resulting from such a suboptimal filter will be inconsistent because they have no knowledge of the incorrect hypothesized model. By explicitly accounting for the deviation of the hypothesis, recursions are developed which, when combined with MMAE are shown to provide superior tracking performance over the standard MMAE. Additionally the proposed variation, called model error MMAE, is shown to provide acceptable tracking performance for dynamically switching systems at a fraction of the computational expense of other algorithms specifically developed for that application. The second variation, referred to as generalized multiple model adaptive estimation (GMMAE), uses an augmented vector of current and past residuals to drive the recursion for the hypothesis conditional probabilities. Necessary for that recursion is evaluation of the time-domain autocovariance matrix of the residual sequence. When filtering linear (and linearized) systems, the autocovariance can be analytically expressed as a function of the system matrices, covariances and filter gain. When filtering nonlinear systems using the Unscented filter, analytic expressions for the autocovariance are not possible.^Motivated to include Unscented filters within the GMMAE framework, a method for calculating the time-domain autocovariance of the residual sequence from an Unscented filter is presented. The proposed method is validated analytically on a simplified system and simulation results are presented using the algorithm for process noise estimation in a planar tracking problem.
Download or read book Practical Implementation of Multiple Model Adaptive Estimation Using Neyman Pearson Based Hypothesis Testing and Spectral Estimation Tools written by Peter D. Hanlon and published by . This book was released on 1996-09-01 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates and develops various modifications to the Multiple Model Adaptive Estimation (MMAE) algorithm. The standard MMAE uses a bank of Kalman filters, each based on a different model of the system. Each of the filters predict the system response, based on its system model, to a given input and form the residual difference between the prediction and sensor measurements of the system response. Model differences in the input matrix, output matrix, and state transition matrix, which respectively correspond to an actuator failure, sensor failure, and an incorrectly modeled flight condition for a flight control failure application, were investigated in this research. An alternative filter bank structure is developed that uses a linear transform on the residual from a single Kalman filter to produce the equivalent residuals of the other Kalman filters in the standard MMAE. A Neyman Pearson based hypothesis testing algorithm is developed that results in significant improvement in failure detection performance when compared to the standard hypothesis testing algorithm. Hypothesis testing using spectral estimation techniques is also developed which provides superior failure identification performance at extremely small input levels.
Download or read book Feynman Kac Formulae written by Pierre Del Moral and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text takes readers in a clear and progressive format from simple to recent and advanced topics in pure and applied probability such as contraction and annealed properties of non-linear semi-groups, functional entropy inequalities, empirical process convergence, increasing propagations of chaos, central limit, and Berry Esseen type theorems as well as large deviation principles for strong topologies on path-distribution spaces. Topics also include a body of powerful branching and interacting particle methods.
Download or read book Flight Control Failure Detection and Control Redistribution Using Multiple Model Adaptive Estimation with Filter Spawning written by Michael L. Torres and published by . This book was released on 2002-03-01 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current research, the MMAE with Filter Spawning and Control Redistribution (MMAE/FS/CR) are used together to identify failures and apply appropriate corrections. This effort explores the performance of the MMAE/FS/CR in different regions of the flight envelope using model and gain scheduling. The MMAE/FS/CR is able to detect complete and partial actuator/surface failures, as well as complete sensor failures. Once the actuator/surface failure is identified and the effectiveness is determined in the case of partial failures, proper control is applied in order to accomplish the desired pilot command. Improvements in the algorithm are required in order to enhance the MMAE/FS/CR ability to detect failures while undergoing maneuvering flight. This investigation shows the ability of the MMAE/FS to detect failures while transitioning through the flight envelope and while performing pilot commanded maneuvers. It also improves and demonstrates the CR ability to compensate for complete or partial actuator/surface failures.
Download or read book Sampled data Kalman Filtering and Multiple Model Adaptive Estimation for Infinite dimensional Continuous time Systems written by Scott A. Sallberg and published by . This book was released on 2007 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kalman filtering and multiple model adaptive estimation (MMAE) methods have been applied by researchers in several engineering disciplines to a multitude of problems as diverse as aircraft flight control and drug infusion monitoring. MMAE methods have been used to adapt to an uncertain noise environment and/or identify important system parameters in these problems. All of the model-based estimation (and control) problems considered in this earlier research have at their core a linear (or mildly nonlinear) model based on finite-dimensional differential (or difference) equations perturbed by random inputs (noise). However, many real-world systems are more naturally modeled using an infinite-dimensional continuous-time linear systems model, such as those most naturally modeled as partial differential equations or time-delayed differential equations. Thus, we are motivated to extend existing finite-dimensional techniques, such as the Kalman filter, to allow the engineer to apply familiar tools to a larger class of problems. First, the infinite-dimensional sampled-data Kalman filter (ISKF), which is a mathematical extension of the finite-dimensional sampled-data Kalman filter, is derived. Next, an algorithm to create an essentially-equivalent finite-dimensional discrete-time model from an infinite-dimensional continuous-time model is constructed by modifying an extension of an existing technique for producing an equivalent discrete-time model for a finite-dimensional system. The resulting model completely captures the important qualities of the original infinite-dimensional description. Finally, an extended example featuring these new tools is presented for a stochastic partial differential equation. Specifically, the temperature profile along a slender rod is estimated using a Kalman filter for the case of a one-dimensional stochastic heat equation with Neumann boundary conditions. Additionally, the MMAE with a bank of Kalman filters is used to estimate the heat profile in the face of an unknown noise environment (zero-mean white Gaussian noises with uncertain covariances in the dynamics and/or measurement models) and to perform system identification (to determine the thermal diffusivity constant) in the face of an unknown noise environment.
Download or read book Flight Control Failure Detection and Control Redistribution Using Multiple Model Adaptive Estimation with Filter Spawning written by and published by . This book was released on 2002 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current research, the MMAE with Filter Spawning and Control Redistribution (MMAE/FS/CR) are used together to identify failures and apply appropriate corrections. This effort explores the performance of the MMAE/FS/CR in different regions of the flight envelope using model and gain scheduling. The MMAE/FS/CR is able to detect complete and partial actuator/surface failures, as well as complete sensor failures. Once the actuator/surface failure is identified and the effectiveness is determined in the case of partial failures, proper control is applied in order to accomplish the desired pilot command. Improvements in the algorithm are required in order to enhance the MMAE/FS/CR ability to detect failures while undergoing maneuvering flight. This investigation shows the ability of the MMAE/FS to detect failures while transitioning through the flight envelope and while performing pilot commanded maneuvers. It also improves and demonstrates the CR ability to compensate for complete or partial actuator/surface failures.
Download or read book Adaptive Estimation and Parameter Identification Using Multiple Model Estimation Algorithm written by Michael Athans and published by . This book was released on 1976 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this report is to introduce an adaptive estimation and parameter identification scheme which the authors call Multiple Model Estimation Algorithm (MMEA). The MMEA consists of a bank of Kalman filters with each matched to a possible parameter vector. The state estimates generated by these Kalman filters are then combined using a weighted sum with the a posteriori hypothesis probabilities as weighting factors. If one of the selected parameter vectors coincides with the true parameter vector, this algorithm gives the minimum variance state and parameter estimates. Algorithms for filtering, smoothing, and prediction are derived for linear and nonlinear systems. They are described in a tutorial fashion with results stated explicitly so that they can be readily used for computer implementation. Approaches for the extension of MMEA to a more general class of adaptive estimation problems are outlined. Several further research topics are also suggested.
Download or read book New Algorithms for Moving Bank Multiple Model Adaptive Estimation written by Juan R. Vasquez and published by . This book was released on 1998-05-01 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard Multiple Model Adaptive Estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. A new moving-bank MMAE algorithm is developed based on exploitation of the density data available from the MMAE. The methods used to exploit this information include various measures of the density data and a decision-making logic used to move, expand, and contract the MMAE bank of filters. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters. A new parameter discretization method is developed based on the probabilities associated with the generalized Chi-Squared random variables formed by residual information from the elemental Kalman filters within the MMAE. Modifications to an existing discretization method are also presented, permitting application of this method in real time and to nonlinear system models or linear/linearized models that are unstable or astable. These new algorithms are validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.
Download or read book Proceedings of the 9th IFToMM International Conference on Rotor Dynamics written by Paolo Pennacchi and published by Springer. This book was released on 2015-05-26 with total page 2214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 9th IFToMM International Conference on Rotor Dynamics. This conference is a premier global event that brings together specialists from the university and industry sectors worldwide in order to promote the exchange of knowledge, ideas, and information on the latest developments and applied technologies in the dynamics of rotating machinery. The coverage is wide ranging, including, for example, new ideas and trends in various aspects of bearing technologies, issues in the analysis of blade dynamic behavior, condition monitoring of different rotating machines, vibration control, electromechanical and fluid-structure interactions in rotating machinery, rotor dynamics of micro, nano and cryogenic machines, and applications of rotor dynamics in transportation engineering. Since its inception 32 years ago, the IFToMM International Conference on Rotor Dynamics has become an irreplaceable point of reference for those working in the field and this book reflects the high quality and diversity of content that the conference continues to guarantee.
Download or read book Application of Multiple Model Adaptive Estimation Algorithms to Maneuver Detection and Estimation written by J. Korn and published by . This book was released on 1983 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiple Model Adaptive Estimation Applied to the LAMBDA URV for Failure Detection and Identification written by David W. Lane (CAPT, USAF.) and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Target Tracking with Random Finite Sets written by Weihua Wu and published by Springer Nature. This book was released on 2023-08-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.
Download or read book Adaptive Estimation Algorithms written by Larry James Levy and published by . This book was released on 1970 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study considers the problem of estimating the states of a linear discrete dynamical system when the covariance matrix, R, of the stationary white sequence corrupting the measurement and/or the covariance matrix, Q, of the stationary white input sequence are unknown. Two new adaptive estimators, called the Reprocessing Filter (RF) and the Maximum A Posteriori (MAP) estimator, are developed which jointly estimate the state variables and the unknown R and/or Q. The new feature common to both estimators is the use of easily implementable estimators of R and/or Q in a reprocessing configuration with the Kalman-filter algorithm. (Author).
