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Full-Text Articles in Physical Sciences and Mathematics
New Tracking Filter Algorithm Using Input Parameter Estimation, Corey M. Broussard
New Tracking Filter Algorithm Using Input Parameter Estimation, Corey M. Broussard
Theses and Dissertations
A new method for the design of tracking filters for maneuvering targets, based on kinematic models and input signals estimation, is developed. The input signal's level, u is considered a continuous variable and consequently the input estimation problem is posed as a purely parameter estimation problem. Moreover, the application of the new tracking filter algorithm is not contingent on distinguishing maneuvering and non-maneuvering targets, and does not require the detection of maneuver onset. The filter will automatically detect the onset of a maneuver. Furthermore, an estimate of the target's acceleration is also obtained with reasonable precision. This opens the door …
Development And Testing Of A High-Speed Real-Time Kinematic Precise Dgps Positioning System Between Two Aircraft, Christopher J. Spinelli
Development And Testing Of A High-Speed Real-Time Kinematic Precise Dgps Positioning System Between Two Aircraft, Christopher J. Spinelli
Theses and Dissertations
This research involves the design, implementation, and testing of a high-speed, real-time kinematic, precise differential GPS positioning system for use in airborne applications such as automated aerial-refueling and close formation flying. Although many of the current ambiguity resolution techniques use the residuals from the least squares position estimation to determine the true ambiguity set, this thesis presents a novel approach to the ambiguity resolution problem, called the minimum indicator. Instead of assuming the ambiguity set with the lowest residuals is the true set, other special characteristics of the residuals are examined. This increases the confidence that the algorithm has selected …
Robot Localization Using Visual Image Mapping, Carrie D. Crews
Robot Localization Using Visual Image Mapping, Carrie D. Crews
Theses and Dissertations
One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to be able to determine its location. The calculation of the robot's pose is an optimization problem making use of the robot's internal navigation sensors and data fusion of range sensor readings to find the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use to avoid obstacles, communicate with other agents in the same environment, and locate targets. Our solution entails mounting a Class 1 laser to an ERS-7 …
A Monocular Vision Based Approach To Flocking, Brian Kirchner
A Monocular Vision Based Approach To Flocking, Brian Kirchner
Theses and Dissertations
Flocking is seen in nature as a means for self protection, more efficient foraging, and other search behaviors. Although much research has been done regarding the application of this principle to autonomous vehicles, the majority of the research has relied on GPS information, broadcast communication, an omniscient central controller, or some other form of "global" knowledge. This approach, while effective, has serious drawbacks, especially regarding stealth, reliability, and biological grounding. This research effort uses three Pioneer P2-AT8 robots to achieve flocking behavior without the use of global knowledge. The sensory inputs are limited to two cameras, offset such that the …
Application Of Fuzzy State Aggregation And Policy Hill Climbing To Multi-Agent Systems In Stochastic Environments, Dean C. Wardell
Application Of Fuzzy State Aggregation And Policy Hill Climbing To Multi-Agent Systems In Stochastic Environments, Dean C. Wardell
Theses and Dissertations
Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually even as the operating environment changes. Applying this learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the policy hill climbing methods of Win …