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This report describes a method of extending the capabilities of a production system applied to tactical situation assessment, to enable the system, whatever its type, to perform the higher order logical reasoning required for the solution of many platform identification problems. The method is implemented through (1) production rules for constructing and updating an intermediate framework in the system's data base, a framework that supports the process of associating tracks with platforms, and (2) production rules for performing the association process by using, for example, the notion of complete subsets of platform files and track files, along with logical sequences of reasoning. Many of the system logic rules underlying this method have been implemented in an experimental production system and tested against hypothesized data. Two examples are given, along with a step-by-step description of the response of the system to each new piece of information. The method as it is described uses only data having high confidence values. However, the conclusions which would logically follow from different assumptions about particular tracks or platforms could be determined by recording logical conclusions derived from less certain data, then reinitializing the data base by removing previous conclusions and replacing selected questionable assertions with alternative assertions.

The representation of unsteady aerodynamic flow fields in terms of global aerodynamic modes has proven to be a useful method for reducing the size of the aerodynamic model over those representations that use local variables at discrete grid points in the flow field. Eigenmodes and Proper Orthogonal Decomposition (POD) modes have been used for this purpose with good effect. This suggests that system identification models may also be used to represent the aerodynamic flow field. Implicit in the use of a systems identification technique is the notion that a relative small state space model can be useful in describing a dynamical system. The POD model is first used to show that indeed a reduced order model can be obtained from a much larger numerical aerodynamical model (the vortex lattice method is used for illustrative purposes) and the results from the POD and the system identification methods are then compared. For the example considered, the two methods are shown to give comparable results in terms of accuracy and reduced model size. The advantages and limitations of each approach are briefly discussed. Both appear promising and complementary in their characteristics.

This thesis considers the problem of how to infer a social network of entities based solely on periodic location information as they move in space and time. Specifically, we consider social networks implied by vessel traffic within the South China Sea as captured by Automatic Identification System data consisting of vessel identifier, position coordinates (latitude and longitude), heading, speed, and other information about its course. We create customized data structures in the Python programming language and implement an algorithm to quickly and efficiently create a social network of entities based on proximity parameters for both space and time. As we see from our analysis, there is no single network depiction of our data. Rather, the topology is largely influenced by the parameters we use to define a connection between entities. Because we can efficiently query the data, we quickly draw conclusions about the most active time of day and most active location within the region. From this, we are able to look into specific entities and examine the relationships, activities, and behaviors that we wish to understand.

This thesis considers the problem of how to infer a social network of entities based solely on periodic location information as they move in space and time. Specifically, we consider social networks implied by vessel traffic within the South China Sea as captured by Automatic Identification System data consisting of vessel identifier, position coordinates (latitude and longitude), heading, speed, and other information about its course. We create customized data structures in the Python programming language and implement an algorithm to quickly and efficiently create a social network of entities based on proximity parameters for both space and time. As we see from our analysis, there is no single network depiction of our data. Rather, the topology is largely influenced by the parameters we use to define a connection between entities. Because we can efficiently query the data, we quickly draw conclusions about the most active time of day and most active location within the region. From this, we are able to look into specific entities and examine the relationships, activities, and behaviors that we wish to understand.

Demanding aerodynamic modeling requirements for military and civilian aircraft have provided impetus for researchers to improve computational and experimental techniques. Model validation is a key component for these research endeavors so this study is an initial effort to extend conventional time history comparisons by comparing model parameter estimates and their standard errors using system identification methods. An aerodynamic model of an aircraft performing one-degree-of-freedom roll oscillatory motion about its body axes is developed. The model includes linear aerodynamics and deficiency function parameters characterizing an unsteady effect. For estimation of unknown parameters two techniques, harmonic analysis and two-step linear regression, were applied to roll-oscillatory wind tunnel data and to computational fluid dynamics (CFD) simulated data. The model used for this study is a highly swept wing unmanned aerial combat vehicle. Differences in response prediction, parameters estimates, and standard errors are compared and discussed