# Browsing by Subject "Kalman filtering"

Now showing 1 - 10 of 10

###### Results Per Page

###### Sort Options

Item Advanced navigation algorithms for precision landing(2007-12) Zanetti, Renato, 1978-; Bishop, Robert H., 1957-Show more A detailed analysis of autonomous navigation algorithms to achieve autonomous precision landing is presented. The problem of integrated attitude determination and inertial navigation is solved. The theoretical results are applied and tested in three different applications. Optimality conditions for constrained quaternion estimation using the Kalman filter are derived. It is common in spacecraft applications to separate the attitude determination from the inertial navigation system. While this approach has worked in the past, it inevitably degrades the navigation performance when the correlations between the two systems are not correctly accounted for. It is shown how to optimally include an attitude determination algorithm into the Kalman filter. When the conditions to achieve optimality are not met, it is shown how to achieve sub-optimality by properly accounting for the correlation. The traditional approach to inertial navigation is to employ the inertial measurement unit (IMU) outputs to propagate the estimated states forward in time, rather then use them to update the state. A detailed covariance analysis of deadreckoning Mars entry navigation is performed. The contribution of various sources of IMU errors are explicitly accounted for and the filter performance is validated through Monte Carlo analysis. The drawback of dead-reckoning is that this approach prevents the inertial measurements from reducing the uncertainty of the estimated states. While this shortcoming can be compensated by the availability of other measurements, it becomes crucial when the IMU is the only sensor to provide measurements. Such a situation arises, for example, during Mars atmospheric entry. In the second application of this work, IMU measurements from a NASA mission are processed in an extended Kalman filter, and the results are compared to dead-reckoning. It is shown that is possible to reduce the uncertainty of the inertial states by filtering the IMU. The final application is lunar descent to landing navigation. In this example the IMU is filtered and the algorithms to include an attitude estimate into the Kalman filter are tested. The design performance is confirmed by Monte Carlo analysis.Show more Item Control and Optimization of a Compact 6-Degree-of-Freedom Precision Positioner Using Combined Digital Filtering Techniques(2012-02-14) Silva Rivas, Jose ChristianShow more This thesis presents the multivariable controller design and implementation for a high-precision 6-degree-of-freedom (6-DOF) magnetically levitated (maglev) positioner. The positioner is a triangular single-moving part that carries three 3-phase permanent-magnet linear-levitation-motor armatures. The three planar levitation motors not only generate the vertical force to levitate the triangular platen but control the platen's position in the horizontal plane. All 6-DOF motions are controlled by magnetic forces only. The positioner moves over a Halbach magnet matrix using three sets of two-axis Hall-effect sensors to measure the planar motion and three Nanogage laser distance sensors for the vertical motion. However, the Hall-effect sensors and the Nanogage laser distance sensors can only provide measurements of the displacement of all 6-axis. Since we do not have full-state feedback, I designed two Linear Quadratic Gaussian (LQG) multivariable controllers using a recursive discrete-time observer. A discrete hybrid H2/H(infinity) filter is implemented to obtain optimal estimates of position and orientation, as well as additional estimates of velocity and angular velocity for all 6 axes. In addition, an analysis was done on the signals measured by the Hall-effect sensors, and from there several digital filters were tested to optimize the readings of the sensors and obtain the best estimates possible. One of the multivariable controllers was designed to close the control loop for the three-planar-DOF motion, and the other to close the loop for the vertical motion, all at a sampling frequency of 800 Hz. Experimental results show a position resolution of 1.5 micrometers with position noise of 0.545 micrometers rms in the x-and y-directions and a resolution of less than 110 nm with position noise of 49.3 nm rms in z.Show more Item Development of multisensor fusion techniques with gating networks applied to reentry vehicles(2003) Dubois-Matra, Olivier; Bishop, Robert H., 1957-Show more The problem of model inaccuracy for Extended Kalman Filters (EKF) is addressed in the case of vehicle atmospheric entry tracking and navigation with a filter bank architecture, also called mixture-of-experts, regulated by gating network, which is then tested in two different applications. First, a wind-frame based flight model is developed, which allows for maneuvers, and inclusion of atmospheric and gravity models. This level of complexity allows in theory for better estimation accuracy when used in an EKF, but the filter performance is in part dependent on the accuracy of the vehicle and environment models. The problem is how to deal with imperfect models. The approach treated here, which as already been applied in other domains, is to create a population of filters, each representing a particular modeling of the vehicle and/or environment. The discriminating device between the expert filters is a gating network, which is a simplified single-layer neural network learning in real-time with the help of the statistical information from the filters. The gating network is used to compute a weighted sum of the state estimate from each filter, which is therefore an optimal estimate. The gating network can also be used as an hypothesis tester, which is the case in the first example. The system was applied to the tracking and identification at high altitude of reentering spiraling objects accompanied by decoys. The object is being tracked at high altitude by three ground radars providing a variety of measurements which are treated in parallel by two filters, one being an expert tuned for the real target and the other tuned for the decoy. Experiments show that the regulated bank can rapidly correctly identify the object as being the real target. The second application is precision Mars entry navigation, where the on-board navigation system of a maneuvering Mars lander used a bank of expert EKF, each processing inertial acceleration as measurement, and each designed around a specific realization of the imperfectly known atmospheric density profile. The objective here is less to identify the best performing model than optimizing the overall state estimate by combining the estimate from every filter. The system also periodically restarts the filters with the current optimal estimate so as to keep all the filters competitive during all of the descent. The result is that this mixture-of-experts does not perform better than a dead-reckoning scheme unless one of the density model happens to be relatively close from the real density profile, but that it is more robust than dead-reckoning to loss of data, and can readily adapt additional sources of measurements.Show more Item An ensemble Kalman filter module for automatic history matching(2007-12) Liang, Baosheng, 1979-; Sepehrnoori, Kamy, 1951-Show more The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required by the practical applications. An automatic history matching module based on the ensemble Kalman filter is developed and validated in this dissertation. The ensemble Kalman filter has three steps: initial sampling, forecasting through a reservoir simulator, and assimilation. The initial random sampling is improved by the singular value decomposition, which properly selects the ensemble members with less dependence. In this way, the same level of accuracy is achieved through a smaller ensemble size. Four different schemes for the assimilation step are investigated and direct inverse and square root approaches are recommended. A modified ensemble Kalman filter algorithm, which addresses the preference to the ensemble members through a nonequally weighting factor, is proposed. This weighted ensemble Kalman filter generates better production matches and recovery forecasting than those from the conventional ensemble Kalman filter. The proposed method also has faster convergence at the early time period of history matching. Another variant, the singular evolutive interpolated Kalman filter, is also applied. The resampling step in this method appears to improve the filter stability and help the filter to deliver rapid convergence both in model and data domains. This method and the ensemble Kalman filter are effective for history matching and forecasting uncertainty quantification. The independence of the ensemble members during the forecasting step allows the benefit of high-performance computing for the ensemble Kalman filter implementation during automatic history matching. Two-level computation is adopted; distributing ensemble members simultaneously while simulating each member in a parallel style. Such computation yields a significant speedup. The developed module is integrated with reservoir simulators UTCHEM, GEM and ECLIPSE, and has been implemented in the framework Integrated Reservoir Simulation Platform (IRSP). The successful applications to two and three-dimensional cases using blackoil and compositional reservoir cases demonstrate the efficiency of the developed automatic history matching module.Show more Item Extended Kalman filtering problem in wildlife telemetry(Texas Tech University, 2001-05) Sugathadasa, Manjula SamanmaleeShow more The problem of accurately locating the position of an animal using noisy directional data is treated here from the viewpoint of extended Kalman filtering methodology. The physical model considered consists of an animal moving randomly in a confined area and the location of it is tracked using several fixed measuring devices, each of which is nominally capable measuring the angular location of the animal from its own location. These angular measurements are inaccurate due to random noise. The system is modelled mathematically as follows. Time is assumed to move in discrete steps which coincide with moments at which measurements are taken. During a given time step, the movement of the animal is described by a linear difference equation driven by random noise, and the inaccuracies of the measuring devices are modelled as additive noise with zero mean and known covariance. The extended Kalman filtering problem for this system is formulated, and theoretical analysis is carried out. It is shown that if the animal movement is suitably confined, then the covariance of estimation errors satisfy a stable dynamical system. In particular, bounds on the magnitude of the covariance of estimation errors are derived. It is also shown that there is an associated extended Kalman filtering problem with stable filter and covariance dynamics. Extensive simulation experiments are carried out to compare the performance of Kalman filtering strategies with well established triangulation methods.Show more Item Investigation of the suitability of parallel processing in local networks for variable-size Kalman filter(Texas Tech University, 1989-12) Ho, Meng WongShow more Not availableShow more Item Kalman filter estimation of corporate earnings(Texas Tech University, 1971-12) Ezzat, Mohamed OmarShow more Not availableShow more Item Navigation algorithms and observability analysis for formation flying missions(2006) Huxel, Paul John; Bishop, Robert H., 1957-Show more Item Spacecraft precision entry navigation using an adaptive sigma point Kalman filter bank(2007) Heyne, Martin Cornelius, 1973-; Bishop, Robert H., 1957-Show more Item The effects of the variances of the aggregate price disturbances on the inflation-output tradeoffs: the United States and the United Kingdom, 1963:I-1987:IV(Texas Tech University, 1993-08) Yamak, RahmiShow more Not availableShow more