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Modeling errors in Kalman filters
Title
Modeling errors in Kalman filters.
Creator
Xu, Hua., Florida Atlantic University, Roth, Zvi S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
This thesis focuses on the performance of the Kalman filters for scalar time-invariant systems when modeling errors are present. A complete classification of errors according to their effect on the filter performance is carried. Certain errors may drive the Kalman filter into instability. Other errors affect only certain statistical properties of the innovations process. Some of the results have been extended to the scalar time-varying and vector time invariant filtering problems.
Date Issued
1988
PURL
http://purl.flvc.org/fcla/dt/14435
Subject Headings
Kalman filtering
Format
Document (PDF)
Nonlinear filtering techniques for failure detection in dynamic systems
Title
Nonlinear filtering techniques for failure detection in dynamic systems.
Creator
Ruokonen, Tuula., Florida Atlantic University, Roth, Zvi S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Developing on-line methods for detecting and locating process malfunctions is an important goal towards full automation of systems. Model-based methods, which check the consistency of the observations using the known functional relationships among the process variables seem to have the potential of early detection of slowly developing failures in complex systems. This dissertation deals with nonlinear filtering as a method for failure detection in dynamic processes. The failure detection...
Show moreDeveloping on-line methods for detecting and locating process malfunctions is an important goal towards full automation of systems. Model-based methods, which check the consistency of the observations using the known functional relationships among the process variables seem to have the potential of early detection of slowly developing failures in complex systems. This dissertation deals with nonlinear filtering as a method for failure detection in dynamic processes. The failure detection problem is presented here as an Identification Problem, where no jumps between the possible operating modes are assumed, i.e. there is only uncertainty with regard to which is the present mode. The identification filter consists of parallel Kalman filters, each tuned to a different system mode of operation, whose estimates parametrize the conditional probability equations. An extension of the filter is derived for the case of different measurement noise coefficients in different modes for the continuous-discrete case. In the continuous-time case with different measurement noise intensities the likelihood function is shown to be ill-defined as the induced measures become singular. The average performance of the continuous-descrete as well as the continuous-time identification filters are studied. It is shown that on the average and after a sufficiently long time a correct decision is expected for any decision threshold level. An alternative identification filter structure is derived using the maximum-likelihood estimation philosophy. The filter reduces to parallel Kalman filters, feeding the state estimates to a maximum likelihood generator which then chooses a set of indicator functions to maximize the total likelihood. Some aspects of interpreting a sequence of decisions, choosing a decision threshold and reinitializing the filter are discussed qualitatively using simulation examples. Furthermore, a new approach based on scaling of the likelihood functions is presented. Scaling is shown to be equivalent to choosing a threshold level for the conditional probabilities.
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Date Issued
1989
PURL
http://purl.flvc.org/fcla/dt/12238
Subject Headings
Kalman filtering
Format
Document (PDF)
post-processing Kalman smoother for underwater vehicle navigation
Title
A post-processing Kalman smoother for underwater vehicle navigation.
Creator
Gustafson, Einar Irgens., Florida Atlantic University, An, Edgar
Abstract/Description
This thesis describes an automated post-processing tool, designed for use on navigational data gathered by Autonomous Underwater Vehicles (AUVs), developed and operated by the Department of Ocean Engineering at Florida Atlantic University. The post-processing tool consists of a 9-state complementary Kalman filter in conjunction with a Rauch-Tung-Striebel (RTS) smoothing algorithm. The Kalman filter is run forward in time to merge navigational data from an Inertial Measurement Unit (IMU), a...
Show moreThis thesis describes an automated post-processing tool, designed for use on navigational data gathered by Autonomous Underwater Vehicles (AUVs), developed and operated by the Department of Ocean Engineering at Florida Atlantic University. The post-processing tool consists of a 9-state complementary Kalman filter in conjunction with a Rauch-Tung-Striebel (RTS) smoothing algorithm. The Kalman filter is run forward in time to merge navigational data from an Inertial Measurement Unit (IMU), a Doppler Velocity Log (DVL), a magnetic compass, a GPS/DGPS system and an Ultrashort Baseline (USBL) tracking system. Subsequently, the RTS smoothing algorithm is run backwards in time to find and compensate for drift errors in dead reckoned position and compass measurement error. The post-processing tool has been implemented as a graphical user interface, designed in MATLAB. Improved accuracy in post-processed position and heading has been verified by conducting sea trials and post-processing the collected data.
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Date Issued
2001
PURL
http://purl.flvc.org/fcla/dt/12752
Subject Headings
Kalman filtering, Underwater navigation
Format
Document (PDF)
KALMAN FILTER MODEL
Title
KALMAN FILTER MODEL.
Creator
Agiakloglou, Christos N., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
Abstract/Description
This thesis demonstrates the theory and the application of the Kalman filter model, a model where the coefficients are not assumed to be constant over time but time-varying. This model is approached in two different ways. The first is the recursive approach and the second is the Mixed estimation approach. Both of these two approaches describe in different ways the original Kalman filter model. This thesis also contains an empirical application of the Kalman filter model, using real data from...
Show moreThis thesis demonstrates the theory and the application of the Kalman filter model, a model where the coefficients are not assumed to be constant over time but time-varying. This model is approached in two different ways. The first is the recursive approach and the second is the Mixed estimation approach. Both of these two approaches describe in different ways the original Kalman filter model. This thesis also contains an empirical application of the Kalman filter model, using real data from the Greek economy to estimate the Demand for Money.
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Date Issued
1987
PURL
http://purl.flvc.org/fcla/dt/14372
Subject Headings
Kalman filtering, Estimation theory
Format
Document (PDF)
Online Parameter Learning for Structural Condition Monitoring System
Title
Online Parameter Learning for Structural Condition Monitoring System.
Creator
Alqazzaz, Jaffar, Jang, Jinwoo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In...
Show moreThe purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From this, the performance of online parameter identification can be tested with respect to different noise levels. This research is based on simulation work showcasing how effective the Kalman filtering techniques are in dealing with analytical uncertainties of data.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013540
Subject Headings
Kalman filtering, Kalman filtering--Data processing, Simulations, Parameter estimation
Format
Document (PDF)
KALMAN FILTERING FOR ROBOTIC CALIBRATION
Title
KALMAN FILTERING FOR ROBOTIC CALIBRATION.
Creator
EL-BALAH, OUSSAMA NAJIB RAWDAH., Florida Atlantic University, Roth, Zvi S.
Abstract/Description
This thesis is concerned with the use of calibration techniques to increase robot accuracy. It is mainly an overview of some of the problems involved in the identification phase of calibration. A robot error model is developed and Kalman filtering algorithm is used in the identification of robot kinematic error parameters. Computer simulations and examples are used to study the behavior of the Kalman filter and its theoretical advantages in robot calibration.
Date Issued
1987
PURL
http://purl.flvc.org/fcla/dt/14370
Subject Headings
Robotics--Calibration, Kalman filtering
Format
Document (PDF)
Automatic extraction and tracking of eye features from facial image sequences
Title
Automatic extraction and tracking of eye features from facial image sequences.
Creator
Xie, Xangdong., Florida Atlantic University, Sudhakar, Raghavan, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the...
Show moreThe dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the processing system. A new corner detection algorithm is presented in which the problem of detecting corners is solved by minimizing a cost function. Each cost factor captures a desirable characteristic of the corner using both the gray level information and the geometrical structure of a corner. This approach additionally provides corner orientations and angles along with corner locations. The advantage of the new approach over the existing corner detectors is that it is able to improve the reliability of detection and localization by imposing criteria related to both the gray level data and the corner structure. The extraction of eye features is performed by using an improved method of deformable templates which are geometrically arranged to resemble the expected shape of the eye. The overall energy function is redefined to simplify the minimization process. The weights for the energy terms are selected based on the normalized value of the energy term. Thus the weighting schedule of the modified method does not demand any expert knowledge for the user. Rather than using a sequential procedure, all parameters of the template are changed simultaneously during the minimization process. This reduces not only the processing time but also the probability of the template being trapped in local minima. An efficient algorithm for real-time eye feature tracking from a sequence of eye images is developed in the dissertation. Based on a geometrical model which describes the characteristics of the eye, the measurement equations are formulated to relate suitably selected measurements to the tracking parameters. A discrete Kalman filter is then constructed for the recursive estimation of the eye features, while taking into account the measurement noise. The small processing time allows this tracking algorithm to be used in real-time applications. This tracking algorithm is suitable for an automated, non-intrusive and inexpensive system as the algorithm is capable of measuring the time profiles of the eye movements. The issue of compensating head movements during the tracking of eye movements is also discussed. An appropriate measurement model was established to describe the effects of head movements. Based on this model, a Kalman filter structure was formulated to carry out the compensation. The whole tracking scheme which cascades two Kalman filters is constructed to track the iris movement, while compensating the head movement. The presence of the eye blink is also taken into account and its detection is incorporated into the cascaded tracking scheme. The above algorithms have been integrated to design an automated, non-intrusive and inexpensive system which provides accurate time profile of eye movements tracking from video image frames.
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Date Issued
1994
PURL
http://purl.flvc.org/fcla/dt/12377
Subject Headings
Kalman filtering, Eye--Movements, Algorithms, Image processing
Format
Document (PDF)
Static error modeling of sensors applicable to ocean systems
Title
Static error modeling of sensors applicable to ocean systems.
Creator
Ah-Chong, Jeremy Fred., Florida Atlantic University, An, Edgar
Abstract/Description
This thesis presents a method for modeling navigation sensors used on ocean systems and particularly on Autonomous Underwater Vehicles (AUV). An extended Kalman filter was previously designed for the implementation of the Inertial Navigation System (INS) making use of Inertial Measurement Unit (IMU), a magnetic compass, a GPS/DGPS system and a Doppler Velocity Log (DVL). Emphasis is put on characterizing the static sensor error model. A "best-fit ARMA model" based on the Aikake Information...
Show moreThis thesis presents a method for modeling navigation sensors used on ocean systems and particularly on Autonomous Underwater Vehicles (AUV). An extended Kalman filter was previously designed for the implementation of the Inertial Navigation System (INS) making use of Inertial Measurement Unit (IMU), a magnetic compass, a GPS/DGPS system and a Doppler Velocity Log (DVL). Emphasis is put on characterizing the static sensor error model. A "best-fit ARMA model" based on the Aikake Information Criterion (AIC), Whiteness test and graphical analyses were used for the model identification. Model orders and parameters were successfully estimated for compass heading, GPS position and IMU static measurements. Static DVL measurements could not be collected and require another approach. The variability of the models between different measurement data sets suggests online error model estimation.
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Date Issued
2003
PURL
http://purl.flvc.org/fcla/dt/12977
Subject Headings
Underwater navigation, Kalman filtering, Error-correcting codes (Information theory), Detectors
Format
Document (PDF)