When hostile missile raids are launched, protecting allied assets requires that many targets be tracked simultaneously. In these raids, it is possible that the number of missiles could outnumber the sensors available to measure them. In these situations, communication between sensors can be utilized along with dynamic task planning to increase the amount of knowledge available concerning these missiles. Since any allied decisions must depend on the knowledge available from the sensors, it follows that improving the overall knowledge will improve the ability of allies to protect their assets through improved decision making. The goal of the this research effort is to create a Sensor Resource Management (SRM) algorithm to optimize the information available during these missile raids, as well as strengthening the simulation framework required to evaluate the performance of the SRM. The SRM must be capable of near-real-time run time so that it could potentially be deployed in a real-world system. The SRM must be capable of providing time-varying assignments to sensors, allowing more than one target to be observed by a single sensor. The SRM must predict measurements based on sensor models to assess the potential information gain by each assignment. Using these predictions, an optimal allocation of all sensors must be constructed. The initial simulation, upon which this work was built, was capable of simulating a set number of missiles launched simultaneously, providing appropriate charts to display the accuracy of knowledge on each target as well as their predicted impact locations. Communication delays are implemented within the simulation, and sensor models are refined. In refining the sensor models, they are given geometric limitations such as range and viewing angles. Additionally, simulated measurements incorporate geometric considerations to provide more realistic values. The SRM is also improved to account for the details added to the simulation. These improvements include creating assignment schedules and allowing a time-varying numbers of targets. The resulting simulation and SRM are presented, and potential future work is discussed. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/26128 |
| Date | 24 September 2014 |
| Creators | Freeze, John Edwin |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0017 seconds