|Student Name:||Anthony Runco, 2d Lt|
|Thesis:||Detection Optimization of the Progressive Multi-Channel Correlation Algorithm Used in Infrasound Nuclear Treaty Monitoring|
|Location:||Bldg 640, Rm 335|
|Date & Time:||02/22/2013 at 1300|
|Abstract:|| This thesis develops methods to determine optimum detection thresholds for the Progressive Multi-Channel Correlation Algorithm (PMCC) used by the International Data Centre (IDC) to perform infrasound station-level event detection. Receiver Operating Characteristic (ROC) curve analysis is used with real ground truth data to determine the trade-off between the probability of detection and the false alarm rate at various consistency detection thresholds. Further, statistical detection theory via maximum likelihood and Bayes cost approaches is used to determine optimum "family" size thresholds of grouped detection "pixels" with similar signal attributes (i.e. trace velocity, azimuth, time of arrival, and frequency content) before the detection should be considered for further processing. Optimum family sizes are determined based upon the consistency threshold and filter configuration used to filter sensor data prior to running the detection algorithm. Finally, this research generates synthetic signals for particular array configurations, adjusts the signal to noise ratio (SNR) to determine the SNR failure levels for the PMCC detection algorithm, and compares this performance to the performance of fielded infrasound stations with similar configurations. For the fielded stations studied, PMCC was able to detect signals with post-filtered SNRs as low as 2 dB.