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Pellett, Andrew. "The Extended Kalman-Consensus Filter." Fogler Library, University of Maine, 2011. http://www.library.umaine.edu/theses/pdf/PellettA2011.pdf.
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Sanchez, M. Juan Jose. "Use of an Extended Kalman Filter." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA238639.
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Thesis (M.S. in Systems Engineering (Electronic Warfare))--Naval Postgraduate School, September 1990.Thesis Advisor(s): Titus, Harold A. Second Reader: Powell, J. R. "September 1990." Description based on title screen as viewed on December 22, 2009. DTIC Descriptor(s): Kalman Filtering, Scenarios, Measurement, Detection, Bearing(Direction), Direction Finding, Algorithms, Tracking, Accuracy, Estimates, Surfaces, Maneuvers, Equations. DTIC Identifier(s): Direction Finding Equipment, Extended Kalman Filters, Electronic Support Measures, Function Plot Program, Program Listing. Author(s) subject terms: Kalman Filter, Direction Finding, ESM systems, Venezuela. Includes bibliographical references (p. 82). Also available in print.
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Back, Per. "Simulering av simulinkmodeller med Extended Kalman Filter." Thesis, KTH, Reglerteknik, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-109457.
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Simulations of simulink models using Kalman filters are often very time-consuming. This problem depends mainly on the fact that the Kalman correction has to be performed at each sample instance through the whole simulation. The goal for this thesis work is to reduce that time-consumption for the filtering part (the integration partis treated in a complementary report) of a simulation. Furthermore a Matlab routine to perform parameter tuning and finally a graphical user interface is developed. The filtering part of the simulation in this thesis is based on an Extended Kalman Filter (EKF). The time optimization of this filter considers searching for the possibility to replace the today’s existing Matlab functions that is used to perform the filtering calculations. Examples of such functions are routines for linearization and integration. To decrease the time-consumption, we have also developed a routine to make it possible to convert a simulink model to a state-space description. This conversion makes it possible to avoid a lot of time-consuming calls to the simulink model. In this case it is the built-in functions in Matlab that causes the large time-consumption. The main time-consuming parts in the filter are the built-in routines for linearization (linmod) and the numerical method that is used to calculate the prediction error (riccatiequation). By creating new routines to solve these problems, the total time-consumption for the filtering part is reduced by approximately a factor of eighteen. As a final step the time optimized Kalman filter and the time optimized integration (treated in a complementary report) are brought together in a time efficient routine for simulation. This final routine for simulation may further be used to perform a time efficient simulation, but also to form a routine, which can be used to estimate unknown parameters in a simulink model. Using the time optimized parts of the simulation routine will make it possible to reduce the execution time for a filtering simulation by approximately a factor of ten. Three kinds of models are used to confirm that the different element of the Kalman filter and the new developed routines work properly. These models consist of one fermentation system that describes a biological process, and two different tank systems that describe the level and the torrent of water in several water tanks.Vid filtrerande simulering av simulinkmodeller är tidsåtgången i dagsläget mycket påtaglig, mest beroende på att kalmankorrigeringen måste appliceras i varje samplingspunkt. Målet med detta examensarbete är att minska tidsåtgången som för närvarande råder för den filtrerande delen (den integrerande delen av simuleringen behandlas i en komplimenterande rapport) av en simulering. Utöver detta utvecklas även en Matlab-rutin för parametersökning samt ett enkelt grafiskt användargränssnitt som underlättar användandet av utvecklade rutiner. Den filtrerande delen av simuleringen består i detta examensarbete av ett s.k. utvidgat kalmanfilter, EKF (Extended Kalman Filter). Tidsoptimeringen av detta filter bygger på att undersöka och eventuellt ersätta de inbyggda Matlabfunktioner som i dagsläget måste användas för att genomföra en sådan filtrering. Exempel på sådana är funktioner för linjarisering och integrering. För att minska tidsåtgången utvecklas även en rutin för konvertering av simulinkmodeller till en s.k. tillståndsbeskrivning. Detta medför bl.a. att tidsödande anrop till simulinkmodellen kan undvikas. De i Matlab inbyggda funktioner som i detta fall står för den största delen av den påtagliga tidsåtgången är linmod för linjarisering samt en inbyggd numerisk metod för att beräkna prediktionsfelets varians (riccati-ekvationen). Genom att skapa nya metoder för att lösa dessa problem, har tidsåtgången för att utföra den filtrerande delen av simuleringen reducerats med en faktor 18. I ett slutskede sammanfogas sedan det tidsoptimerade kalmanfiltret med en tidsoptimerad rutin för integrering (behandlas i en komplimenterande rapport) till en komplett simuleringsrutin. Denna simuleringsrutin kan sedan användas för tidseffektiva simuleringar av simulinkmodeller, men utnyttjas även som grundstomme vid utveckling av parametersökningsrutinen. Den sammanfogade simuleringsrutinen har med hjälp av de två tidsoptimerade delarna för kalmanfiltrering och integrering medfört att tidsåtgången för att genomföra en filtrerande simulering reducerats med ungefär en faktor 10. För att testa de olika momenten och de utvecklade rutinerna används tre olika modeller. Dessa modeller består av ett fermatorsystem som beskriver en biologisk tillväxtprocess samt två olika tanksystem som beskriver flöden och nivåer i det aktuella systemets vattentankar.
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Magnusson, Thom. "State Estimation of UAV using Extended Kalman Filter." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93931.
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In unmanned systems an autopilot controls the outputs of the vehicle withouthuman interference. All decisions made by the autopilot will depend on estimatesdelivered by an Inertial Navigation System, INS. For the autopilot to take correctdecisions it must rely on correct estimates of its orientation, position and velocity.Hence, higher performance of the autopilot can be achieved by improving its INS.Instrument Control Sweden AB has an autopilot developed for fixed wing aircraft.The focus of this thesis has been on investigating the potential benefits of usingExtended Kalman filters for estimating information required by the control systemin the autopilot. The Extended Kalman filter is used to fuse sensor measurementsfrom accelerometers, magnetometers, gyroscopes, GPS and pitot tubes. The filterwill be compared to the current Attitude and Heading Reference System, AHRS, tosee if better results can be achieved by utilizing sensor fusion.
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Pettersson, Martin. "Extended Kalman Filter for Robust UAV Attitude Estimation." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119097.
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Attitude estimation of unmanned aerial vehicles is of great importance as it enables propercontrol of the vehicles. Attitude estimation is typically done by an attitude-heading refer-ence system (ahrs) which utilises different kind of sensors. In this thesis these include agyroscope providing angular rates measurements which can be integrated to describe the at-titude as well as an accelerometer and a magnetometer, both of which can be compared withknown reference vectors to determine the attitude. The sensor measurements are fused usinga gps augmented 7-state Extended Kalman filter (ekf) with a quaternion and gyroscope bi-ases as state variables. It uses differentiated gps velocity measurements to estimate externalaccelerations as reference vector to the accelerometer, which significantly raises robustnessof the solution. The filter is implemented in MatlabTM and in c on an ARM microprocessor.It is compared with an explicit complementary filter solution and is evaluated with flightsusing a fixed-wing uav with satisfactory results.
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Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.
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The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.
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Brett, Daniel J. "Orbital parameter estimation using an extended Kalman filter." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/26680.
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The problem of orbital parameter estimation using angles only observations is examined. Direction cosine measurements, obtained from satellite passage of an earth-based stationary planar radar beam, are assimilated by an extended Kalman filter to improve estimates of a classical orbital element set. Several progressively comprehensive orbital motion models are considered and compared. The relative effectiveness of these models is illustrated by applying them to actual satellite data..
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Masinjila, Ruslan. "Multirobot Localization Using Heuristically Tuned Extended Kalman Filter." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35489.
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A mobile robot needs to know its pose (position and orientation) in order to navigate and perform useful tasks. The problem of determining this pose with respect to a global or local frame is called localisation, and is a key component in providing autonomy to mobile robots. Thus, localisation answers the question Where am I? from the robot’s perspective. Localisation involving a single robot is a widely explored and documented problem in mobile robotics. The basic idea behind most documented localisation techniques involves the optimum combination of noisy and uncertain information that comes from various robot’s sensors. However, many complex robotic applications require multiple robots to work together and share information among themselves in order to successfully and efficiently accomplish certain tasks. This leads to research in collaborative localisation involving multiple robots. Several studies have shown that when multiple robots collaboratively
localise themselves, the resulting accuracy in their estimated positions and orientations outperforms that of a single robot, especially in scenarios where robots do not have access to information about their surrounding environment.
This thesis presents the main theme of most of the existing collaborative, multi-robot localisation solutions, and proposes an alternative or complementary solution to some of the existing challenges in multirobot localisation. Specifically, in this thesis, a heuristically tuned Extended Kalman Filter is proposed to localise a group of mobile robots. Simulations show that when certain conditions are met, the proposed tuning method significantly improves the accuracy and reliability of poses estimated by the Extended Kalman Filter. Real world experiments performed on custom-made robotic platforms validate the simulation results.
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Marins, Joõ Luʹis. "An extended Kalman filter for quaternion-based attitude estimation." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA384973.
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Thesis (Degree in Electrical Engineer and M.S. in Electrical Engineering) Naval Postgraduate School, Sept. 2000.Thesis advisor(s): Yun, Xiaoping; Backman, Eric R.; Hutchins, Robert G. "September 2000." Includes bibliographical references (p. 91). Also available in print.
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Marins, Joao L. "An extended Kalman filter for quaternion-based attitude estimation." Thesis, Monterey, California. Naval Postgraduate School, 2000. http://hdl.handle.net/10945/9411.
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The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with those angles. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are in turn integrated to obtain quaternions. The outputs of the model are values of three-dimensional angular rates, three-dimensional linear accelerations, and three-dimensional magnetic field vector. Gauss-Newton iteration is utilized to find the best quaternion that relates the measured linear accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. The quaternion obtained from the optimization algorithm is used as part of the observations for the Kalman filter. As a result, the measurement equations become linear. A new approach to attitude estimation is introduced in this thesis. The computational requirements related to the extended Kalman filter developed using this approach are significantly reduced, making it possible to estimate attitude in real-time. Extensive static and dynamic simulation of the filter using Matlab proved it to be robust. Test cases included the presence of large initial errors as well as high noise levels. In all cases the filter was able to converge and accurately track attitude.
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Sandink, Christopher Albert. "Screening diagnostics for parameter selection in extended Kalman filter implementations." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0006/MQ45297.pdf.
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Hayes, Elizabeth Jo Volovecky Cicci David A. "Identification of a tethered satellite using an extended Kalman filter." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2007%20Fall%20Theses/Volovecky_Elizabeth_5.pdf.
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Foun, Kevin. "Identification of civil structural parameters using the extended Kalman filter." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/57987.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 175-179).
In the context of civil and industrial structures, structural control and damage detection have recently become an area of great interest. The safety of a structure is always the most important issue for structural engineers, and to achieve this goal, the discipline of Structural Health Monitoring (SHM) was introduced. SHM records real-time information concerning structural conditions and performances. In order to evaluate the health conditions of structures, identifying the structural parameters is needed. Research activities of this area are increasing due to the availability of computation and wireless technologies. The objective of this thesis is to evaluate the tracking ability of the Kalman filter for identifying civil structural parameters based on measured vibration data which usually are earthquake accelerations. For linear elastic structures, the ordinary Kalman filter was used, but for nonlinear elastic structures, we implemented the extended Kalman filter.
(cont.) For simulating damage occurrence in structures, a sudden change of stiffness was introduced, and an adaptive extended Kalman filter was utilized to estimate the time-varying parameters. In this thesis, linear and nonlinear structures with single-degree-of-freedom and multi-degree-of-freedom were simulated. Measurements having different levels of white noise were considered in order to evaluate the effects of noise on parametric estimations. In addition, the impacts of different levels of noise covariance were also discussed. Simulation results from different structural models were presented to demonstrate the effectiveness of the Kalman filter.
by Kevin Foun.
S.M.
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Raja, Muneeb Masood. "Extended Kalman Filter and LQR controller design for quadrotor UAVs." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1496152489565477.
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Gautam, Ishwor. "Quaternion based attitude estimation technique involving the extended Kalman filter." University of Akron / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1556196539847396.
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Iltis, Ronald A. "NEAR-FAR RESISTANT PSEUDOLITE RANGING USING THE EXTENDED KALMAN FILTER." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/606488.
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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, CaliforniaPseudolites have been proposed for augmentation/replacement of the GPS system in radiolocation applications. However, a terrestrial pseudolite system suffers from the near-far effect due to received power disparities. Conventional code tracking loops as employed in GPS receivers are unable to suppress near-far interference. Here, a multiuser code tracking algorithm is presented based on the extended Kalman filter (EKF.) The EKF jointly tracks the delays and amplitudes of multiple received pseudolite waveforms. A modified EKF based on an approximate Bayesian estimator (BEKF) is also developed, which can in principle both acquire and track code delays, as well as detect loss-of-lock. Representative simulation results for the BEKF are presented for code tracking with 2 and 5 users.
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Spehn, Stephen L. "Noise adaptation and correlated maneuver gating of an extended Kalman filter." Thesis, Monterey, Calif. : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA221713.
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Thesis (M.S. in Electrical Engineering and Electrical Engineer)--Naval Postgraduate School, March 1990.Thesis Advisor(s): Titus, Harold A. ; Loomis, Herschel H. "March 1990." Description based on signature page as viewed on August 24, 2009. DTIC Descriptor(s): Adaptive Filters, Kalman Filtering, Range Gating, Noise Modulation, Adaptation, Dynamics, Estimates, Monte Carlo Method, Noise, Observation, Position(Location), Power Spectra, Simulation, Targets, Tracking, Variations, Theses. Author(s) subject terms: Kalman Filter ; Maneuver Gating ; Noise Adaptation. Includes bibliographical references (p. 139). Also available online.
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Ahrens, Henning [Verfasser]. "The inverse problem of Magnetocardiography - Activation time imaging with the Extended Kalman Filter and the Unscented Kalman Filter / Henning Ahrens." Kiel : Universitätsbibliothek Kiel, 2016. http://d-nb.info/1119802849/34.
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Jones, Philip Andrew. "Techniques in Kalman Filtering for Autonomous Vehicle Navigation." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/78128.
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This thesis examines the design and implementation of the navigation solution for an autonomous ground vehicle suited with global position system (GPS) receivers, an inertial measurement unit (IMU), and wheel speed sensors (WSS) using the framework of Kalman filtering (KF). To demonstrate the flexibility of the KF several methods are explored and implemented such as constraints, multi-rate data, and cascading filters to augment the measurement matrix of a main filter. GPS and IMU navigation are discussed, along with common errors and disadvantages of each type of navigation system. It is shown that the coupling of sensors, constraints, and self-alignment techniques provide an accurate solution to the navigation problem for an autonomous vehicle. Filter divergence is discussed during times when the states are unobservable. Post processed data is analyzed to demonstrate performance under several test cases, such as GPS outage, and the effect that the initial calibration and alignment has on the accuracy of the solution.Master of Science
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Balzer, Dirk. "Online-Demodulation stark gestörter winkelmodulierter Signale mit dem Extended-Kalman-Filter." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=958729964.
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Davis, Robert B. "Applying Cooperative Localization to swarm UAVs using an extended Kalman Filter." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/43900.
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Approved for public release; distribution is unlimitedCooperative Localization (CL) is a process by which autonomous vehicles operating as a team estimate the position of one another to compensate for errors in the positioning sensors used by a single agent. By combining independent measurements originating from members of the team, a single estimate of increased accuracy will result. This approach has the potential to enhance the positional accuracy of an agent over use of a standard GPS, which would be essential for behaviors within a swarm requiring precision move-ments such as maintaining close formation. CL can also provide accurate positional information to the entire group when operating in an intermittent or denied GPS environment. In this thesis, a distributed CL algorithm is implemented on a swarm of Unmanned Aerial Vehicles (UAVs) using an Extended Kalman Filter. Using a technique created for ground robots, the equations are modified to adapt the algorithm to aerial vehicles, and then operation of the algorithm is demonstrated in a centralized system using AR Drones and the Robot Operating System. During tests, the positional accuracy of the UAV using CL improved over use of dead reckoning. However, the performance is not as expected based on the results noted from the referenced two-dimensional application of the al-gorithm. It is presumed that the sensors on-board the AR Drone are responsible. Since the platform is simply a low-cost solution to show proof-of-concept, it is concluded that the implementation of CL presented in this thesis is a suitable approach for enhancing positional accuracy of UAVs within a swarm.
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Lommel, Peter Hans. "An extended Kalman filter extension of the augmented Markov decision process." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32453.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 99-102).
As the field of robotics continues to mature, individual robots are increasingly capable of performing multiple complex tasks. As a result, the ability for robots to move autonomously through their environments is a fundamental necessity. If perfect knowledge of the robot's position is available, the robot motion planning problem can be solved efficiently using any of a number of existing algorithms. Frequently though, the robot's position can only be estimated using incomplete and imperfect information from its sensors and an approximate model of its dynamics. Algorithms which assume perfect knowledge of the robot's position can still be applied by treating the mean or maximum likelihood estimate of the robot's position as certain. However, unless the uncertainty in the agent's position is very small, this approach is not reliable. In order to perform optimally in this situation, planners, such as the partially observable Markov decision process, plan over the entire set of beliefs (distributions over the robot's position). Unfortunately, this approach is only tractable for problems with very few states. Between these two extreme approaches, however, lies a continuum of possible planners which plan over a subset of the belief space. The difficulty that these planners face is choosing and representing a minimal subset of the belief space which spans the set of beliefs that the robot will actually experience. In this paper, we show that there exists a very natural such set, the set, of Gaussian beliefs. By combining an extended Kalman filter with an augmented Markov decision process, we create a path planner which efficiently plans over a discrete approximation of the set of Gaussian beliefs.
(cont.) The resulting planner is demonstrated via simulation to be both computationally tractable and robust to uncertainty in the robot's position.
by Peter Hans Lommel.
S.M.
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Kallapur, Abhijit Aerospace Civil & Mechanical Engineering Australian Defence Force Academy UNSW. "A discrete-time robust extended kalman filter for estimation of nonlinear uncertain systems." Publisher:University of New South Wales - Australian Defence Force Academy. Information Technology & Electrical Engineering, 2009. http://handle.unsw.edu.au/1959.4/44095.
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This thesis provides a novel approach to the problem of state estimation for discrete-time nonlinear systems in the presence of large model uncertainties. Though classical nonlinear Kalman filters such as the extended Kalman filter (EKF) can handle uncertainties by increasing the value of noise covariances, this is only applicable to systems with small uncertainties. To this end, a discretetime robust extended Kalman filter (REKF) is formulated and applied to examples from the fields of aerospace engineering and signal processing with an emphasis on attitude estimation for small unmanned aerial vehicles (UAVs) and image processing under the influence of atmospheric turbulence. The robust filter is an approximate set-valued state estimator where the Riccati and filter equations are obtained as an approximate solution to a reverse-time optimal control problem defining the set-valued state estimator. The advantages of the REKF over the classical EKF are investigated for examples from the fields aerospace engineering and signal processing where large model uncertainties are introduced. In the case of small UAVs, an alternative attitude estimation algorithm based on the REKF is proposed in the event of gyroscopic failure and the inability of the vehicle to carry redundant sensors due to limited payload capabilities. In the case of image reconstruction under atmospheric turbulence, a robust pixel-wandering (random shifts) scheme is proposed to aid the process of image reconstruction. Also, problems pertaining to platform vibration analysis for aerospace vehicles and a frequency demodulation process in the presence of channel-induced uncertainties is also discussed.
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Isaac, Benson. "Inverse Kinematics and Extended Kalman Filter based Motion Tracking of Human Limb." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406809906.
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Fakoorian, Seyed Abolfazl. "Ground Reaction Force Estimation in Prosthestic Legs with an Extended Kalman Filter." Cleveland State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=csu148227120124386.
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Wheeler, David Orton. "Relative Navigation of Micro Air Vehicles in GPS-Degraded Environments." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6609.
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Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.
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Shah, Tejas Jagdish. "Online parameter estimation applied to mixed conduction/radiation." Thesis, Texas A&M University, 2005. http://hdl.handle.net/1969.1/2361.
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The conventional method of thermal modeling of space payloads is expensive and cumbersome. Radiation plays an important part in the thermal modeling of space payloads because of the presence of vacuum and deep space viewing. This induces strong nonlinearities into the thermal modeling process. There is a need for extensive correlation between the model and test data. This thesis presents Online Parameter Estimation as an approach to automate the thermal modeling process. The extended
Kalman fillter (EKF) is the most widely used parameter estimation algorithm for
nonlinear models. The unscented Kalman filter (UKF) is a new and more accurate technique for parameter estimation. These parameter estimation techniques have been evaluated with respect to data from ground tests conducted on an experimental space payload.
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Chen, Haofei. "Estimation of model parameters in twisted string actuation system via extended kalman filter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17860/.
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This paper is based on a twisted string actuation system that allows the implementation of simple, powerful, compact and lightweight tendon-based driving systems by using small-size DC motors as actuators characterized by high speed and low torque. This actuation system is well-suited for the implementation in highly-robotic devices due to its properties. The results obtained with this simple and compact actuation system can be used in the field of robotic devices such as robotic hands and exoskeletons.
In this paper, the basic working principle of the actuation system is introduced, and the constitutive equations of the system are presented. Then the dynamic model of the actuation system is built, and the dynamic equations are derived. Furthermore. An extended Kalman filter is used in the system in order to estimate the states and some unknown parameters of the system. At last, a control algorithm based on feedback linearization and regulated by the extended Kalman filter is evaluated by the simulation, this algorithm guarantees a high robustness against disturbances.
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Wiklander, Jonas. "Performance comparison of the Extended Kalman Filter and the Recursive Prediction Error Method." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1832.
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In several projects within ABB there is a need of state and parameter estimation for nonlinear dynamic systems. One example is a project investigating optimisation of gas turbine operation. In a gas turbine there are several parameters and states which are not measured, but are crucial for the performance. Such parameters are polytropic efficiencies in compressor and turbine stages, cooling mass flows, friction coefficients and temperatures. Different methods are being tested to solve this problem of system identification or parameter estimation. This thesis describes the implementation of such a method and compares it with previously implemented identification methods. The comparison is carried out in the context of parameter estimation in gas turbine models, a dynamic load model used in power systems as well as models of other dynamic systems. Both simulated and real plant measurements are used in the study.
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Hartana, Pande. "Comparison of linearized and extended Kalman filter in GPS-aided inertial navigation system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57729.pdf.
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Buyamin, Salinda. "Optimisation of the extended Kalman filter for speed estimation of induction motor drives." Thesis, University of Newcastle Upon Tyne, 2007. http://hdl.handle.net/10443/672.
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A speed sensorless drive requires the elimination of the sensor; therefore a speed estimator is required. Speed estimation using the Extended Kalman Filter (EKF) is investigated. The use of an EKF as an observer for a sensorless induction motor has been a longstanding subject of research. However, little attempt has been made to optimise the filter performance. First some speed estimation results are presented where the commonly used Trial and Error method is used for tuning the EKF. The performance of the EKF is strictly dependant on the choice of the covariance matrices. Therefore to improve the performance of the EKF, a guided random search technique, Simulated Annealing is proposed. The work concentrates on finding the EKF parameters by the Simulated Annealing algorithm in both low and high performance drives, for constant V/f and vector control. A Genetic Algorithm is also a guided random search technique and in this work the algorithm has been used for comparison purposes on optimising the EKF. The robustness of the EKF parameters tuned by Genetic Algorithm, Simulated Annealing and Trial and Error is compared. The results presented show that Simulated Annealing is more robust against machine parameter variations. Despite the large computation time Simulated Annealing does have the potential of being an alternative method for optimising the EKF. These novel results presented here show that Simulated Annealing is capable of tuning the EKF in the induction motor drives application.
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Hartana, Pande (Pande Putu Gde) Carleton University Dissertation Engineering Mechanical and Aerospace. "Comparison of linearized and extended Kalman filter in GPS aided inertial navigation system." Ottawa, 2000.
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Pettersson, Hanna. "Estimation and Pre-Processing of Sensor Data in Heavy Duty Vehicle Platooning." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79038.
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Today, a rapid development towards fuel efficient technological aids for vehicles is in progress. One step towards this is the development of platooning systems. The main concept of platooning is to let several heavy duty vehicles (HDVs) drive in a convoy and share important information with each other via wireless communication. This thesis describes one out of three subsystems in a project developed to handle the process from raw sensor data to control signal. The goal of the project is to achieve a safe and smooth control with the main purpose of reduced fuel consumption. This subsystem processes the raw sensor data received from the different HDVs. The purpose is to estimate the positions and velocities of the vehicles in a platoon, taking into account that packet-loss, out of sequence measurements and irrelevant information can occur. This is achieved by filtering the information from different sensors in an Extended Kalman Filter and converting it into a local coordinate system with the origin in the ego vehicle. Moreover, the estimates are sorted and categorized into classes with respect to the status of the vehicles. The result of the thesis is useful estimates that are independent of outer effects in a local reference system with origin in the host vehicle. This information can then be used for further sensor fusion and implementation of a Model Predictive Controller (MPC) in two other subsystems. These three subsystems result in a smooth and safe control with an average reduced fuel consumption of approxi- mately 11.1% when the vehicles drive with a distance of 0.5 seconds in a simulated environment.Dagens utveckling inom fordonsindustrin fokuserar mer och mer påutveckling av bränsleeffektiva hjälpmedel. Ett steg i denna riktning är utvecklingen av platooningsystem. Huvudkonceptet med platooning är att låta flera tunga fordon köra i följd i en konvoj och dela viktig information med varandra via trådlös kommuni- kation och en automatiserad styrstrategi. Detta examensarbete beskriver ett utav tre delsystem i ett projekt som är utvecklat för att hantera en process från rå sensordata till styrsignaler för fordonen. Målet är att uppnå en säker och mjuk reglering med huvudsyftet att reducera bränsleförbrukningen. Det här delsystemet behandlar mottagen sensordata från de olika fordonen. Målet med delsystemet är att skatta positioner och hastigheter för fordonen i konvojen med hänsyn till att förlorad, försenad eller irrelevant information från det trådlösa nätverket kan förekomma. Detta uppnås genom filtrering i ett Extended Kalman Filter och konvertering till ett lokalt referenssystem med origo i det egna fordo- net. Utöver detta sorteras informationen och kategoriseras in i olika klasser efter fordonens status. Examensarbetet resulterade i användbara skattningar oberoende av yttre om- ständigheter i ett lokalt referenssystem med origo i det egna fordonet. Denna information kan användas vidare för ytterligare sensorfusion och implementering av en modellbaserad prediktionsregulator (MPC) i två andra delsystem. De tre delsystemen resulterade i en mjuk och säker reglering och en reducerad bränsleför- brukning med i genomsnitt 11.1% då fordonen körde med 0.5 sekunders avstånd i en simulerad miljö.
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Jonsson, Holm Erik. "Vehicle Mass and Road Grade Estimation Using Kalman Filter." Thesis, Linköpings universitet, Fordonssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70266.
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This Master's thesis presents a method for on-line estimation of vehicle mass and road grade using Kalman filter. Many control strategies aiming for better fuel economy, drivability and safety in today's vehicles rely on precise vehicle operating information. In this context, vehicle mass and road grade are crucial parameters. The method is based on an extended Kalman filter (EKF) and a longitudinal vehicle model. The main advantage of this method is its applicability on drivelines with continuous power output during gear shifts and cost effectiveness compared to hardware solutions. The performance has been tested on both simulated data and on real measurement data, collected with a truck on road. Two estimators were developed; one estimates both vehicle mass and road grade and the other estimates only vehicle mass using an inclination sensor as an additional measurement. Tests of the former estimator demonstrate that a reliable mass estimate with less than 5 % error is often achievable within 5 minutes of driving. Furthermore, the root mean square error of the grade estimate is often within 0.5°. Tests of the latter estimator show that this is more accurate and robust than the former estimator with a mass error often within 2 %. A sensitivity analysis shows that the former estimator is fairly robust towards minor modelling errors. Also, an observability analysis shows under which circumstances simultaneous vehicle mass and road grade is possible.
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Martinsson, Patrik. "State of Charge Estimation in a High Temperature Sodium Nickel Chloride Battery Using Kalman Filter." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11173.
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In today’s heavy industry there are applications demanding high power supply in certain periods of a working cycle. A typical case might be startup of heavy machinery or just keeping a certain point in a distribution network at a certain energy level. To deal with this different techniques might be used, one way is to introduce a battery as an energy reserve in the system. One battery studied at ABB for this purpose is the so called High Temperature Sodium Nickel Chloride battery and a model of this battery has been developed at ABB. When operating a battery of the mentioned type in an application it is important to keep track of the energy stored in the battery. Earlier tests has shown that this is difficult in a noisy environment.
This master thesis investigates if a Kalman filter may be used to estimate the energy stored in the battery. The investigation is performed in steps, starting with a simplified model of the battery and then expanding to a more complete model. Evaluation of the methods and algorithms used is made by simulations and based on the assumption that there is a good model available. The model is special in such a way that it has a varying number of states despite that the number of outputs remains the same.
Some comparisons with actual measurements are also made and an analysis of the parameters in the model along with an introduction to the system identification problem is discussed, assuming that the structure of the model is correct.
I dagens tunga industri finns applikationer som kräver höga effektuttag under vissa perioder av en arbetscykel. Ett typiskt fall kan vara uppstart av tunga maskiner eller att hålla en given spänningsnivå i en belastningspunkt i ett distributionsnät. För att hantera detta finns olika metoder, en möjlighet är att använda ett batteri som en energireserv. Ett högtemperaturbatteri har studerats på ABB för detta ändamål och en model av detta batteri har tagits fram. När ett sådant batteri används är det viktigt att kontinuerligt veta hur mycket energi som finns till förfogande i batteriet. Tidigare tester har visat att detta är svårt i en brusig miljö.
I detta examensarbete kommer det undersökas om ett Kalman filter kan användas för att skatta energin i detta batteri. Undersökningen sker i steg och startar med en förenklad modell som sedan utvecklas till en mer komplett modell. Utvärdering av de metoder och algoritmer som används sker via simuleringar och baseras på antagandet att modellen är komplett och riktig. Denna modell är speciell på det sätt att den har ett variabelt antal tillstånd trots att antalet utsignaler är konstant.
Viss jämförelse med de mätningar som finns tillgängliga görs och en inledande analys av de ingående modellparametrarna presenteras. Även en introduktion till det omfattande systemidentifieringsproblemet diskuteras, med antagandet att modellens struktur är korrekt.
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Olayanju, Iyeyinka Damilola, and Olabode Paul Ojelabi. "Using Multilateration and Extended Kalman Filter for Localization of RFID Passive Tag in NLOS." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1119.
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The use of ubiquitous network has made real time tracking of objects, animals and human beings easy through the use of radio frequency identification system (RFID). Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. The tags consist of a small chip and a printed antenna which receives from and transmits information to the reader. The range information about the distance between the tag and the reader is obtained from the received signal strength indication (RSSI). Accuracy of the read range using RSSI can be very complicated especially in complicated propagation environment due to the nature and features of the environment. There are different kinds of localisation systems and they are Global Positioning System (GPS) which can be used for accurate outdoor localization; while technologies like artificial vision, ultrasonic signals, infrared and radio frequency signals can be employed for indoor localization. This project focuses on the location estimation in RFID Non Line-of-Sight (NLOS) environment using Real Time Localization System (RTLS) with passive tags, in carrying out passengers and baggage tracking at the airport. Indoor location radio sensing suffers from reflection, refraction and diffractions due to the nature of the environment. This unfavourable phenomenon called multipath leads to delay in the arrival of signal and the strength of signal received by receiving antenna within the propagation channel which in turns affects the RSSI, yielding inaccurate location estimation. RTLS based on time difference of arrival and error compensation technique and extended Kalman filter technique were employed in a NLOS environment to determine the location of tag. The better method for location estimation in a NLOS between the Kalman filtering and extended Kalman filtering is investigated. According to simulation results, the extended Kalman filtering technique is more suitable to be applied to RTLS.
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Uner, Muhittin. "Frequency, amplitude, and phase tracking of nonsinusoidal signal in noise with extended Kalman filter." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28235.
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Approved for public release; distribution is unlimitedThis thesis applies extended Kalman filtering to the problem of estimating frequency, amplitude, and phase of a nonsinusoidal periodic signal contaminated by additive white, Gaussian noise. Parameters will be estimated up to mth significant harmonic component. It also gives an approach for the case of less than mth significant harmonic components. The estimator will track the signal's fundamental frequency, amplitudes, and phases while these parameters are changing slowly over time. The amplitudes are estimated as if the fundamental frequency estimate is correct; the frequency and the phases of the signal are estimated as if the amplitude estimation is correct. This thesis also contains tracking and the capture behavior of the filter.
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Idkhajine, Lahoucine. "Fully FPGA-based Sensorless Control for synchronous AC drive using an Extended Kalman Filter." Thesis, Cergy-Pontoise, 2010. http://www.theses.fr/2010CERG0506/document.
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L'objectif du travail réalisé dans le cadre de cette thèse est de montrer l'intérêt d'utiliser les FPGAs (Field Programmable Gate Array) comme support pour l'implantation d'algorithmes complexes dédiés à la commande de machines électriques. Pour ce faire, une commande sans capteur mécanique utilisant un filtre de Kalman étendu et basée sur FPGA est réalisée. Cette commande est destinée à piloter une machine synchrone à pôles saillants. Le modèle d-q de la machine basé sur l'approximation d'inertie infinie est implanté. L'ordre du Filtre de Kalman est donc égal à 4 et la complexité totale de la boucle de régulation est évaluée à près de 700 opérations arithmétiques (dont plus de 53% de multiplications). Les apports des solutions FPGAs en termes de performances de contrôle et en termes de capacité d'intégration sont quantifiés.En terme de performances de contrôle, il a été démontré qu'en utilisant de telles solutions matérielles, le temps de calcul est très réduit (de l'ordre de 5µs, 5% de la période d'échantillonnage). Cette rapidité de calcul permet d'avoir un contrôle quasi-instantané ce qui améliore la bande passante de la boucle de régulation. A ce sujet, une comparaison avec les performances obtenues avec une solution logicielle telle que le DSP est effectuée. Dans les deux cas, le comportement dynamique de la boucle de régulation s de vitesse ans capteur est quantifié.En termes de capacité d'intégration, il est possible de développer une architecture commune qui peut être adaptée à plusieurs systèmes. A titre d'exemple, il est possible de développer un filtre de Kalman sur un même FPGA capable d'estimer les grandeurs de plusieurs systèmes sans pour autant affecter les performances de contrôle. En outre, une méthodologie de développement dédiée à de tels algorithmes complexes est proposée. Il s'agit là d'une adaptation des méthodologies proposées dans des travaux de thèse précédents, [62] et [63]. En effet, une étape de spécification préliminaire du système ainsi que des procédures d'optimisation supplémentaires y sont introduites. Ces dernières sont particulièrement nécessaires dans le cas de commandes complexes et permettent une adéquation entre l'algorithme développé et l'architecture FPGA correspondante. De plus, cette méthodologie a été organisée de façon à distinguer l'étape du développement de l'algorithme et l'étape du développement de l'architecture FPGA. Un état de l'art sur les technologies FPGA est également proposé. La structure interne des FPGAs récents est décrite. Leur contribution dans le domaine de la commande des machines électriques est quantifiée. Les différentes étapes de la méthodologie de développement sont présentées. Le développement d'une commande numérique (basée sur FPGA) d'une machine synchrone à aimant permanent associée à un capteur de position Resolver est par la suite traité. Cette application s'inscrit dans un contexte avionique où l'objectif était d'avoir une solution FPGA hautement intégrée. Pour ce faire, le FPGA Actel Fusion est utilisé. Ce composant intègre un convertisseur analogique numérique. La commande, le traitement des signaux Resolver ainsi que la conversion analogique numériques sont implantés sur le même composant.En ce qui concerne la commande sans capteur basée sur le filtre de Kalman étendu, il a été décidé de structurer les chapitres correspondants à travers la méthodologie de développement proposée. Ainsi, la phase de spécification préliminaire du système, la phase du développement de l'algorithme, la phase du développement de l'architecture FPGA et la phase d'expérimentation sont séparément traitées. Durant la phase d'expérimentation, la procédure «Hardware-In-the-Loop (HIL)» est incluse afin de valider le fonctionnement de l'architecture développée une fois la phase d'implantation physique achevéeThe aim of this thesis is to present the interest of using Field Programmable Gate Array (FPGA) devices for the implementation of complex AC drive controllers. The case of a sensorless speed controller using the Extended Kalman Filter (EKF) has been chosen and applied to a Salient Synchronous Machine (SSM). The d-q model based on the infinite inertia hypothesis has been implemented. The corresponding EKF order is then equal to 4 and the complexity of the whole sensorless controller is equal to 700 arithmetic operations (more than 53% of multiplications). The contribution of FPGAs in this field has been quantified in terms of control performances and in terms of system integration. In terms of control performances, the proposed FPGA-based solution ensures a short execution time which is around 5µs (5% of the sampling period). This treatment fastness ensures a quasi-instantaneous control which improves the control bandwidth. To this purpose, a comparison with a software DSP-based solution is made. The dynamic behavior and the influence of the execution time, in both cases, on the control bandwidth have been quantified. In terms of integration capacity, it is possible to implement a generic FPGA architecture that can be adapted to the control of several systems. Thus, it is possible to develop a common EKF architecture that is able to estimate variables from many systems without affecting the control performances.In addition, a design methodology adapted to such complex controllers has been proposed. The particularity of this updated methodology, compared to the previous ones ([62], [63]), is to provide an enlarged set of steps starting from the preliminary system specification to the ultimate experimentation. Optimization procedures have also been introduced. These optimizations are necessary in case of complex controllers and lead to the adequation between the developed algorithm and the corresponding hardware FPGA architecture. A state of the art FPGA technology is also presented. The internal structure of the recent devices and their corresponding technology are discussed. Their contribution in the field of AC drive applications is quantified. An in-depth presentation of the proposed design methodology is made.Besides, the development of a fully integrated FPGA-based controller for a Permanent Magnet Synchronous Machine (PMSM) associated with a Resolver sensor is presented. This controller has been developed in for an aircraft application where the main objective was to develop a fully integrated FPGA solution. The Actel Fusion FPGA device has been used. This device integrates an Analog to Digital Converter (ADC). The current controller, the Resolver Processing Unit (RPU) and the analog to digital conversion are implemented within the same device. When it comes to the sensorless controller, the corresponding chapters have been structured according to the presented design methodology: the preliminary system specification, the algorithm development, the FPGA architecture development and finally the experimentation. The latter includes Hardware-In-the-Loop (HIL) tests and the final experimental validation
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Johnson, Hyrum David 1972. "Real-time identification for ground vehicle trajectory estimation using extended Kalman filter residual analysis." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80015.
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Zhang, Haizheng Ph D. Massachusetts Institute of Technology. "Constrained extended Kalman filter : an efficient improvement of calibration for dynamic traffic assignment models." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104148.
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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 83-86).
The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has an advantage of adapting changes compared to fixed covariance matrix setting in GLS. The contributions of this thesis include: 1) conducting online calibration algorithms on a large network with relatively high dimensional parameters, 2) identifying drawbacks of a widely applied solution for online DTA calibration in a large network, 3) improving an existing algorithm from non-convergence to great performance, 4) proposing an efficient and simple method for the improved algorithm, 5) attaining better performance than an existing benchmark algorithm.
by Haizheng Zhang.
S.M. in Transportation
S.M.
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Ginsberg, Fredrik. "Optimizing multi-robot localization with Extended Kalman Filter feedback and collaborative laser scan matching." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48748.
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Localization is a critical aspect of robots and their industrial applications, with its major impact on navigation and planning. The goal of this thesis is to improve multi-robot localization by utilizing scan matching algorithms to calculate a corrected pose estimate using the robots' shared laser scan data. The current pose estimate relative to the map is used as the initial guess for the scan matching. This corrected pose is fused using several different localization configurations, such as an Extended Kalman Filter in combination with the Adaptive Monte Carlo Localization algorithm. Simulations showed that localization improved by resetting the Monte Carlo particle filter with the pose estimate generated by the collaborative scan matching. Further, in simulated scenarios, the collaborative scan matching implementation improved the accuracy of typical Monte Carlo Localization configurations. Furthermore, when filtering based on the number of reciprocal correspondences between the scan match output and the target scan, one could extract highly accurate pose estimates. When resetting the Monte Carlo Localization algorithm with the pose estimates, the localization algorithm could successfully recover from severe errors in the global positioning. In future work, additional testing needs to be done using these extracted pose estimates in a dedicated map-based multi-robot localization algorithm.
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Sun, Leqiang. "Streamflow and Soil Moisture Assimilation in the SWAT model Using the Extended Kalman Filter." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34975.
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Numerical models often fail to accurately simulate and forecast a hydrological state in operation due to its inherent uncertainties. Data Assimilation (DA) is a promising technology that uses real-time observations to modify a model's parameters and internal variables to make it more representative of the actual state of the system it describes. In this thesis, hydrological DA is first reviewed from the perspective of its objective, scope, applications and the challenges it faces. Special attention is then given to nonlinear Kalman filters such as the Extended Kalman Filter (EKF). Based on a review of the existing studies, it is found that the potential of EKF has not been fully exploited.
The Soil and Water Assessment Tool (SWAT) is a semi-distributed rainfall-runoff model that is widely used in agricultural water management and flood forecasting. However, studies of hydrological DA that are based on distributed models are relatively rare because hydrological DA is still in its infancy, with many issues to be resolved, and linear statistical models and lumped rainfall-runoff models are often used for the sake of simplicity. This study aims to fill this gap by assimilating streamflow and surface soil moisture observations into the SWAT model to improve its state simulation and forecasting capability. Unless specifically defined, all ‘forecasts’ in Italic font are based on the assumption of a perfect knowledge of the meteorological forecast. EKF is chosen as the DA method for its solid theoretical basis and parsimonious implementation procedures.
Given the large number of parameters and storage variables in SWAT, only the watershed scale variables are included in the state vector, and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori/a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Two case studies are carried out with real observation data in order to verify the effectiveness of EKF assimilation. The upstream section of the Senegal River (above Bakel station) in western Africa is chosen for the streamflow assimilation, and the USDA ARS Little Washita experimental watershed is chosen to examine surface soil moisture assimilation. In the case of streamflow assimilation, a spinoff study is conducted to compare EKF state-parameter assimilation with a linear autoregressive (AR) output assimilation to improve SWAT’s flood forecasting capability. The influence of precipitation forecast uncertainty on the effectiveness of EKF assimilation is discussed in the context of surface soil moisture assimilation.
In streamflow assimilation, EKF was found to be effective mostly in the wet season due to the weak connection between runoff, soil moisture and the curve number (CN2) in dry seasons. Both soil moisture and CN2 were significantly updated in the wet season despite having opposite update patterns. The flood forecast is moderately improved for up to seven days, especially in the flood period by applying the EKF subsequent open loop (EKFsOL) scheme. The forecast is further improved with a newly designed quasi-error update scheme.
Comparison between EKF and AR output assimilation in flood forecasting reveals that while both methods can improve forecast accuracy, their performance is influenced by the hydrological regime of the particular year. EKF outperformed the AR model in dry years, while AR outperformed the EKF in wet years. Compared to AR, EKF is more robust and less sensitive to the length of the forecast lead time. A combined EKF-AR method provides satisfying results in both dry and wet years.
The assimilation of surface soil moisture is proved effective in improving the full profile soil moisture and streamflow estimate. The setting of state and observation vector has a great impact on the assimilation results. The state vector with streamflow and all-layer soil moisture outperforms other, more complicated state vectors, including those augmented with intermediate variables and model parameters. The joint assimilation of surface soil moisture and streamflow observation provides a much better estimate of soil moisture compared to assimilating the streamflow only. The updated SWAT model is sufficiently robust to issue improved forecasts of soil moisture and streamflow after the assimilation is ‘unplugged’. The error quantification is found to be critical to the performance of EKF assimilation. Nevertheless, the application of an adaptive EKF shows no advantages over using the trial and error method in determining time-invariant model errors.
The robustness of EKF assimilation is further verified by explicitly perturbing the precipitation ‘forecast’ in the EKF subsequent forecasts. The open loop model without previous EKF update is more vulnerable to erroneous precipitation estimates. Compared to streamflow forecasting, soil moisture forecasting is found to be more resilient to erroneous precipitation input.
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Farahmand, Ashil Sayyed. "Cooperative Decentralized Intersection Collision Avoidance Using Extended Kalman Filtering." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/36276.
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Automobile accidents are one of the leading causes of death and claim more than 40,000 lives annually in the US alone. A substantial portion of these accidents occur at road intersections. Stop signs and traffic signals are some of the intersection control devices used to increase safety and prevent collisions. However, these devices themselves can contribute to collisions, are costly, inefficient, and are prone to failure. This thesis proposes an adaptive, decentralized, cooperative collision avoidance (CCA) system that optimizes each vehicle's controls subject to the constraint that no collisions occur. Three major contributions to the field of collision avoidance have resulted from this research. First, a nonlinear 5-state variable vehicle model is expanded from an earlier model developed in [1]. The model accounts for internal engine characteristics and more realistically approximates vehicle behavior in comparison to idealized, linear models. Second, a set of constrained, coupled Extended Kalman Filters (EKF) are used to predict the trajectory of the vehicles approaching an intersection in real-time. The coupled filters support decentralized operation and ensure that the optimization algorithm bases its decisions on good, reliable estimates. Third, a vehicular network based on the new WAVE standard is presented that provides cooperative capabilities by enabling intervehicle communication. The system is simulated against today's common intersection control devices and is shown to be superior in minimizing average vehicle delay.Master of Science
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Lowe, Matthew. "Extended and Unscented Kalman Smoothing for Re-linearization of Nonlinear Problems with Applications." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/457.
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The Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF) are commonly implemented practical solutions for solving nonlinear state space estimation problems; all based on the linear state space estimator, the Kalman Filter. Often, the UKF and EnKF are cited as a superior methods to the EKF with respect to error-based performance criteria. The UKF in turn has the advantage over the EnKF of smaller computational complexity. In practice however the UKF often fails to live up to this expectation, with performance which does not surpass the EKF and estimates which are not as robust as the EnKF. This work explores the geometry of alternative sigma point sets, which form the basis of the UKF, contributing several new sets along with novel methods used to generate them. In particular, completely novel systems of sigma points that preserve higher order statistical moments are found and evaluated. Additionally a new method for scaling and problem specific tuning of sigma point sets is introduced as well as a discussion of why this is necessary, and a new way of thinking about UKF systems in relation to the other two Kalman Filter methods. An Iterated UKF method is also introduced, similar to the smoothing iterates developed previously for the EKF. The performance of all of these methods is demonstrated using problem exemplars with the improvement of the contributed methods highlighted.
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MEHROTRA, SUMIT. "ENHANCEMENT AND BIAS COMPENSATION IN THE EXTENDED KALMAN OBSERVER AS A PARAMETER ESTIMATOR." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990726300.
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Taylor, Michael Eric. "System identification and control of an Arleigh Burke Class Destroyer using an extended Kalman Filter." Thesis, Springfield, Va. : Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA379628.
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Carlsson, Jesper. "Enhancement of Positioning and Attitude Estimation Using Raw GPS Data in an Extended Kalman Filter." Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-109336.
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A Global Positioning System (GPS) can be used to estimate an objects position,given that the object has a GPS antenna. However, the system requires informationfrom at least four independent satellites in order to be able to give a positionestimate. If two GPS antennas and a carrier-phase GPS measurement unit is usedan estimate of the objects heading can be calculated by determine the baselinebetween the two antennas. The method is called GPS Attitude Determination(GPSAD) and requires that an Integer Ambiguity Problem (IAP) is solved. Thismethod is cheaper than more traditional methods to calculate the heading butis dependent on undisturbed GPS-reception. Through support from an InertialMeasurement Unit (IMU), containing accelerometers and gyroscopes, the systemcan be enhanced. In Thorstenson [2012] data from GPS, GPSAD and IMU wasintegrated in an Extended Kalman Filter (EKF) to enhance the performance. Thisthesis is an extension on Thorstensons work and is divided into two separate problems:enhancement of positioning when less than four satellites are available andthe possibility to integrate the EKF with the search of the correct integers for theIAP in order to enhance the estimation of attitude. For both problems an implementationhas been made and the performance has been enhanced for simulateddata. For the first problem it has been possible to enhance the performance onreal data while that has not been possible for the second problem. A number ofproposals is given on how to enhance the performance for the second problemusing real data.
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Yu, Jiang-Ming, and 余江明. "Extended-Kalman-Filter-Based Chaotic Communication." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/65726432400675398641.
Full textAbstract:
碩士國立成功大學
電機工程學系碩博士班
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An extended-Kalman-filter-based chaotic communication is first proposed in this thesis. First, the optimal linearization technique is utilized to find the exact linear models of the chaotic system at operating states of interest. Then, an extended Kalman filter (EKF) algorithm is used to estimate both the parameters and states where the message is already embedded. By the extended Kalman filter together with the optimal linear model, the message can be recovered well at the receiver’s end. Numerical examples and simulations are given to show the effectiveness of the proposed methodology.
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McFerrin, Melinda Ruth. "Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter." Thesis, 2009. http://hdl.handle.net/2152/ETD-UT-2009-08-365.
Full textAbstract:
Small satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often they must be autonomous. For lunar navigation, contact with a ground station is not always available and the system needs to be robust.
The extended Kalman filter is a nonlinear estimator that has been used on-board spacecraft for decades. The filter requires linear approximations of the state and measurement models. In the past few years, the unscented Kalman filter has become popular and has been shown to reduce estimation errors. Additionally, the Jacobian matrices do not need to be derived in the unscented Kalman filter implementation. The intent of this research is to explore the capabilities of the extended Kalman filter and the unscented Kalman filter for use as a navigation algorithm on small satellites.
The filters are applied to a satellite orbiting the Moon equipped with an inertial measurement unit, a sun sensor, a star camera, and a GPS-like sensor. The position, velocity, and attitude of the spacecraft are estimated along with sensor biases for the IMU accelerometer, IMU gyroscope, sun sensor and star camera. The estimation errors are compared for the extended Kalman filter and the unscented Kalman filter for the position, velocity and attitude.
The analysis confirms that both navigation algorithms provided accurate position, velocity and attitude. The IMU gyroscope bias was observable for both filters while only the IMU accelerometer bias was observable with the extended Kalman filter. The sun sensor biases and the star camera biases were unobservable. In general, the unscented Kalman filter performed better than the extended Kalman filter in providing position, velocity, and attitude estimates but requires more computation time.text
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"Guaranteed properties of the extended Kalman filter." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1987. http://hdl.handle.net/1721.1/3031.
Full textAbstract:
Daniel B. Grunberg, Michael Athans.Cover title. "This paper has been submitted for publication to the Journal of Mathematics of Control, Signals, and Systems."
Includes bibliographical references.
Supported by the NASA Ames and Langley Research Centers. NASA NAG 2-297