Subsurface explosive hazard detection using MIMO forward-looking ground penetrating radar
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This research develops a machine learning algorithm for subsurface object detection on multiple-input-multiple-output (MIMO) forward-looking ground-penetrating radar (FLGPR). By detecting hazards using FLGPR, standoff distances of up to tens of meters can be acquired, but this is at the degradation of performance due to high false alarm rates. The proposed system utilizes an anomaly detection prescreener to identify potential object locations. Alarm locations have log-Gabor statistical features and spectral features, among others, extracted from multiple polarizations. The ability of these features to reduce the number of false alarms and increase the probability of detection is evaluated with data from an arid U.S. Army test site. After doing so, dimensionality reduction is explored for the extracted feature vectors. Finally, the ability to combine entire feature vectors corresponding to a range of feature types and extracted from numerous polarizations is observed. Classification is performed by a Support Vector Machine (SVM) with lane-based cross-validation for training and testing. Class imbalance and optimized SVM kernel parameters are considered during classifier training.