Performance Analysis of Detection System Design Algorithms

dc.contributor.authorNyberg, Karl-Johanen
dc.contributor.committeechairKoelling, C. Patricken
dc.contributor.committeecochairKobza, John E.en
dc.contributor.committeememberBish, Ebru K.en
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.description.abstractDetection systems are widely used in industry. Designers, operators and users of these systems need to choose an appropriate design, based on the intended usage and the operating environment. The purpose of this research is to analyze the effect of various system design variables (controllable) and system parameters (uncontrollable) on the performance of detection systems. To optimize system performance one must manage the tradeoff between two errors that can occur. A False Alarm occurs if the detection system falsely indicates a target is present and a False Clear occurs if the detection system falsely fails to indicate a target is present. Given a particular detection system and a pre-specified false clear (or false alarm) rate, there is a minimal false alarm (or false clear) rate that can be achieved. Earlier research has developed methods that address this false alarm, false clear tradeoff problem (FAFCT) by formulating a Neyman-Pearson hypothesis problem, which can be solved as a Knapsack problem. The objective of this research is to develop guidelines that can be of help in designing detection systems. For example, what system design variables must be implemented to achieve a certain false clear standard for a parallel 2-sensor detection system for Salmonella detection? To meet this objective, an experimental design is constructed and an analysis of variance is performed. Computational results are obtained using the FAFCT-methodology and the results are presented and analyzed using ROC (Receiver Operating Characteristic) curves and an analysis of variance. The research shows that sample size (i.e., size of test data set used to estimate the distribution of sensor responses) has very little effect on the FAFCT compared to other factors. The analysis clearly shows that correlation has the most influence on the FAFCT. Negatively correlated sensor responses outperform uncorrelated and positively correlated sensor responses with large margins, especially for strict FC-standards (FC-standard is defined as the maximum allowed False Clear rate). Suggestions for future research are also included. FC-standard is the second most influential design variable followed by grid size.en
dc.description.degreeMaster of Scienceen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectAirport Securityen
dc.subjectFactorial Designen
dc.subjectCorrelation Modelen
dc.subjectFederal Aviation Administrationen
dc.subjectConditional Distribution Methoden
dc.subjectMain Effectsen
dc.subjectInteraction Effectsen
dc.subjectEstimation of probability distributionsen
dc.subjectHypothesis Testingen
dc.titlePerformance Analysis of Detection System Design Algorithmsen
dc.typeThesisen and Systems Engineeringen Polytechnic Institute and State Universityen of Scienceen


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