Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 128-136).

Michael Triantafyllou, Marc Bodson, Michael Athans. / "July, 1982." / Bibliography: p. 118-120. / National Aeronautics and Space Administration and Langely Research Grant NGL-22-009-124

In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings. A sequence of increasingly complex empirical tests verifies the efficacy of this technique. / Singapore-MIT Alliance (SMA)

Ph.D. / Electrical and Computer Engineering / Numerous applications require either the estimation or prediction of a noisy time-series. Examples include speech enhancement, economic forecasting, and geophysical modeling. A noisy time-series can be described in terms of a probabilistic model, which accounts for both the deterministic and stochastic components of the dynamics. Such a model can be used with a Kalman filter (or extended Kalman filter) to estimate and predict the time-series from noisy measurements. When the model is unknown, it must be estimated as well; dual estimation refers to the problem of estimating both the time-series, and its underlying probabilistic model, from noisy data. The majority of dual estimation techniques in the literature are for signals described by linear models, and many are restricted to off-line application domains. Using a probabilistic approach to dual estimation, this work unifies many of the approaches in the literature within a common theoretical and algorithmic framework, and extends their capabilities to include sequential dual estimation of both linear and nonlinear signals. The dual Kalman filtering method is developed as a method for minimizing a variety of dual estimation cost functions, and is shown to be an effective general method for estimating the signal, model parameters, and noise variances in both on-line and off-line environments.

To reduce large motion responses of moored platforms in a harsh environment in deep waters, a thruster-assisted position mooring system can be applied. By applying the system, global dynamic responses can be improved in terms of the mooring line/riser top tensions, operational radii, and the top and bottom angle of the production risers. Kalman filtering as an optimum observer and estimator for stochastic disturbances is implemented in the developed control algorithm to filter out wave frequency responses. Investigation of the performance of thruster-assisted moored offshore platforms was conducted in terms of six-degree-of-freedom motions and mooring line/riser top tensions by means of a fully coupled hull/mooring/riser dynamic analysis program in the time domain and a spectral analysis. The two cases, motion analyses of a platform with thrusters and without thrusters, are extensively compared. The numerical examples illustrate that for deepwater position-keeping of platforms a thruster-assisted moored platform can be an effective solution compared to a conventionally moored platform.

The estimation approaches examined in this dissertation focus on manipulating system dynamical models to allow the well-known form of the continuous-discrete extended Kalman filter (CDEKF) to accommodate constrained and hybrid systems. This estimation algorithm filters sequential discrete measurements for nonlinear continuous systems modeled with ordinary differential equations. The aim of the research is to broaden the class of systems for which this common tool can be easily applied. Equality constraints, holonomic or nonholonomic, or both, are commonly found in the system dynamics for vehicles, spacecraft, and robotics. These systems are frequently modeled with differential algebraic equations. In this dissertation, three tools for adapting the dynamics of constrained systems for implementation in the CDEKF are presented. These strategies address (1) constrained systems with quasivelocities, (2) kinematically constrained redundant coordinate systems, and (3) systems for which an equality constraint can be broken. The direct linearization work for constrained systems modeled with quasi-velocities is demonstrated to be particularly useful for systems subject to nonholonomic constraints. Concerning redundant coordinate systems, the "constraint force" perspective is shown to be an effective approximation for facilitating implementation of the CDEKF while providing similar performance to that of the fully developed estimation scheme. For systems subject to constraint violation, constraint monitoring methods are presented that allow the CDEKF to autonomously switch between constrained and unconstrained models. The efficacy of each of these approaches is shown through illustrative examples. Hybrid dynamical systems are those modeled with both finite- and infinite-dimensional coordinates. The associated governing equations are integro-partial differential equations. As with constrained systems, these governing equations must be transformed in order to employ the CDEKF. Here, this transformation is accomplished through two finite-dimensional representations of the infinite-dimensional coordinate. The application of these two assumed modes methods to hybrid dynamical systems is outlined, and the performance of the approaches within the CDEKF are compared. Initial simulation results indicate that a quadratic assumed modes approach is more advantageous than a linear assumed modes approach for implementation in the CDEKF. The dissertation concludes with a direct estimation methodology that constructs the Kalman filter directly from the system kinematics, potential energy, and measurement model. This derivation provides a straightforward method for building the CDEKF for discrete systems and relates these direct estimation ideas to the other work presented throughout the dissertation. Together, this collection of estimation strategies provides methods for expanding the class of systems for which a proven, well-known estimation algorithm, the extended Kalman filter, can be applied. The accompanying illustrative examples and simulation results demonstrate the utility of the methods proposed herein.

Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. Consequently, Volvo Construction Equipment endeavour to apply the concept of autonomous vehicles onto one of their main products. In the company’s Autonomous Machine project an autonomous wheel loader is being developed. As an ob jective given by the company; a demonstration proving the possibility of conducting a fully autonomous load and haul cycle should be performed. Conducting such cycle requires the vehicle to be able to localise itself in its task space and navigate accordingly. In this Master’s Thesis, methods of solving those requirements are proposed and evaluated on a real wheel loader. The approach taken regarding localisation, is to apply sensor fusion, by extended Kalman filtering, to the available sensors mounted on the vehicle, including; odometric sensors, a Global Positioning System receiver and an Inertial Measurement Unit. Navigational control is provided through an interface developed, allowing high level software to command the vehicle by specifying drive paths. A path following controller is implemented and evaluated. The main objective was successfully accomplished by integrating the developed localisation and navigational system with the existing system prior this thesis. A discussion of how to continue the development concludes the report; the addition of a continuous vision feedback is proposed as the next logical advancement.

The continuous challenge to decrease emissions, sensor costs and fuel consumption in diesel engines is battled in this thesis. To reach higher goals in engine efficiency and environmental sustainability the prediction of engine states is essential due to their importance in engine control and diagnosis. Model output will be improved with help from sensors, advanced mathematics and non linear Kalman filtering. The task consist of constructing non linear Kalman Filters and to adaptively weight measurements against model output to increase estimation accuracy. This thesis shows an approach of how to improve estimates by nonlinear Kalman filtering and how to achieve additional information that can be used to acquire better accuracy when a sensor fails or to replace existing sensors. The best performing Kalman filter shows a decrease of the Root Mean Square Error of 75 % in comparison to model output.

Multiple Hypothesis Tracking (MHT) is generally the preferred data association technique for tracking targets in clutter and with missed detections due to its increased accuracy over conventional single-scan techniques such as Nearest Neighbor (NN) and Probabilistic Data Association (PDA). However, this improved accuracy comes at the price of greater complexity. Sequential K-best MHT is a simple implementation of MHT that attempts to achieve the accuracy of multiple hypothesis tracking with some of the simplicity of single-frame methods. Our first major objective is to determine under what general conditions Sequential K-best data association is preferable to Probabilistic Data Association. Both methods are implemented for a single-target, single-sensor scenario in two spatial dimensions. Using the track loss ratio as our primary performance metric, we compare the two methods under varying false alarm densities and missed-detection probabilities. Upon implementing a single-target Sequential K-best MHT tracker, a fundamental problem was observed in which the tracks coalesce. The second major thrust of this research is to compare different approaches to resolve this issue. Several methods to detect track coalescence, mostly based on the Mahalanobis and Kullback-Leibler distances, are presented and compared.

The thesis is mainly focused on a hybrid location system, which processes wireless and inertial measurements by extended Kalman filtering. Inertial location system is usually used with Dead-Reckoning method, which calculates the present location and heading direction from a previous known state by using measurements of accelerometer and gyroscope, which have immunity from the environment. The system estimates the position by integrates the measurements of sensors, resulting in high accuracy during a short period. However, the unreliability grows with time due to the bias effect on sensors. By combining the wireless location and inertial system, the uncertainty of estimation can be reduced. In wireless communications, the locations of base stations and the times of signal arrival can be used in locating a mobile station. However, signal propagation could be blocked by objects. The non-line of sight (NLOS) effects cause arrival delay and is usually modeled as exponential distributions. Previously, the improved biased Kalman filters were designed to mitigate the NLOS effect in base station measurements. The system design has difficulty in accommodating inertial measurements. The inertial has immunity to the environment. The property is of help in the NLOS mitigation. Therefore, we propose a hybrid location system that integrating the wireless and inertial measurements by using a hybrid biased extended Kalman filter at the stage of positioning. The system provides better prediction with the assistance of enviroment-free inertial measurements. The NLOS mitigation with prediction feedback scheme results in better mitigation performance. Simulations of different situations have been conducted based on parameters in the IEEE 802.15.3a ultra-wideband environment. The performance differences between the proposed method and other approaches show that inertial assisted system effectively reduces the NLOS effects. Also, the proposed hybrid location system has more efficient mitigation performance and better tracking results.