Change Detection in Constellations of Buried Objects Extracted From Ground-Penetrating Radar Data

Detection of deliberately buried objects in ground-penetrating radar (GPR) data acquired along a path is a clutter-limited problem. Detection-false alarm rate performance can be improved by replacing the detection statistic with a change statistic that incorporates information from previous path traversals. A constellation matching approach is developed for buried-object change detection in GPR data. Network topologies of buried objects detected in GPR data from previous path traversals are maintained in a constellation database. Localized groups of buried objects newly detected on the latest path traversal are matched to the constellation. Buried objects from the latest path traversal whose locations or strengths cannot be reconciled with the constellation are identified as changes. The system has one component that generates constellation databases offline and another component suitable for change detection in real time. It can tolerate paths with significant translational misalignments. The system uses the following: 1) a customized translational relaxation algorithm for point pattern matching that incorporates detection strength and a probabilistic uncertainty model for buried-object location into the objective function and 2) a change statistic that accounts for the magnitude of change relative to predicted detection strength. A constellation database can typically be generated offline from a single path traversal roughly two orders of magnitude faster than the time typically required for a vehicle to travel the extent of the path. Database sizes are typically four to five orders of magnitude smaller than the data sets of GPR signal scans or focused 3-D GPR images that they were generated from. On bumpy dirt roads buried exclusively with nonmetallic objects at various depths, detection-false alarm rate performance is shown to be significantly better for our change statistics than for our detection statistics.