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Full-Text Articles in Engineering
Consistency Results For The Roc Curves Of Fused Classifiers, Kristopher S. Bjerkaas
Consistency Results For The Roc Curves Of Fused Classifiers, Kristopher S. Bjerkaas
Theses and Dissertations
The U.S. Air Force is researching the fusion of multiple classifiers. Given a finite collection of classifiers to be fused, one seeks a new classifier with improved performance. An established performance quantifier is the Receiver Operating Characteristic (ROC) curve, which allows one to view the probability of detection versus the probability of false alarm in one graph. Previous research shows that one does not have to perform tests to determine the ROC curve of this new fused classifier. If the ROC curve for each individual classifier has been determined, then formulas for the ROC curve of the fused classifier exist …
Target Recognition Using Late-Time Returns From Ultra-Wideband, Short-Pulse Radar, Kenneth J. Pascoe
Target Recognition Using Late-Time Returns From Ultra-Wideband, Short-Pulse Radar, Kenneth J. Pascoe
Theses and Dissertations
The goal of this research is to develop algorithms that recognize targets by exploiting properties in the late-time resonance induced by ultra-wide band radar signals. A new variant of the Matrix Pencil Method algorithm is developed that identifies complex resonant frequencies present in the scattered signal. Kalman filters are developed to represent the dynamics of the signals scattered from several target types. The Multiple Model Adaptive Estimation algorithm uses the Kalman filters to recognize targets. The target recognition algorithm is shown to be successful in the presence of noise. The performance of the new algorithms is compared to that of …
Target Recognition Using Linear Classification Of High Range Resolution Radar Profiles, Ricardo A. Diaz
Target Recognition Using Linear Classification Of High Range Resolution Radar Profiles, Ricardo A. Diaz
Theses and Dissertations
High Range Resolution (HRR) radar profiles map three-dimensional target characteristics onto one-dimensional signals that represent reflected radar intensity along target extent. In this thesis, second through fourth statistical moments are extracted from HRR profiles and input to Fisher Linear Discriminant (FLD) classifiers. An iterative classification process is applied that gradually minimizes required a priori knowledge about the target data. It is found that the second through fourth statistical moments of HRR profiles are useful features in the FLD classification of dissimilar targets and they provide reasonable discrimination of similar targets. Greater than 69% correct classification for two-target scenarios and greater …
Characterization Of The Target-Mount Interaction In Radar Cross Section Measurement Calibrations, Donald W. Powers
Characterization Of The Target-Mount Interaction In Radar Cross Section Measurement Calibrations, Donald W. Powers
Theses and Dissertations
Radar Cross Section (RCS) measurements are quintessential in understanding target scattering phenomenon. The reduced RCS of modern weapons systems stresses the capability of current RCS measurement ranges. A limiting factor that has recently become more significant is the electromagnetic coupling between a test target and the mounting hardware used to support it and control its orientation during the RCS measurement. Equally important is the electromagnetic coupling between the RCS calibration artifact and its mount, which provides an opportunity to explore the coupling phenomena without delving into operationally sensitive areas. The primary research goal was to characterize the interaction between a …
An Investigation Of The Effects Of Correlation, Autocorrelation, And Sample Size In Classifier Fusion, Nathan J. Leap
An Investigation Of The Effects Of Correlation, Autocorrelation, And Sample Size In Classifier Fusion, Nathan J. Leap
Theses and Dissertations
This thesis extends the research found in Storm, Bauer, and Oxley, 2003. Data correlation effects and sample size effects on three classifier fusion techniques and one data fusion technique were investigated. Identification System Operating Characteristic Fusion (Haspert, 2000), the Receiver Operating Characteristic Within Fusion method (Oxley and Bauer, 2002), and a Probabilistic Neural Network were the three classifier fusion techniques; a Generalized Regression Neural Network was the data fusion technique. Correlation was injected into the data set both within a feature set (autocorrelation) and across feature sets for a variety of classification problems, and sample size was varied throughout. Total …