Multisensor fusion of FLGPR and thermal and visible-spectrum cameras for standoff detection of buried objects

摘要

Buried targets pose a serious threat to modern soldiers and civilians alike, thus detecting them from a safe standoff distance is an important step in their remediation. Many successful vehicle-based detection systems have been designed to utilize forward-looking ground penetrating radar (FLGPR) for buried target detection at a distance, however, FLGPR has an inherently low signal-to-clutter-ratio (SCR) so its performance is limited. To address this limitation, suites of sensors have been added to some of these vehicle-based systems. In this work we utilize data from these various sensors to improve the buried target classification accuracy. Specifically, we present features extracted from FLGPR, lidar, and thermal- and visible-spectrum camera data, then fuse the various features using a kernel-based classifier. Our results indicate that fusing these multimodal features yields a higher classification performance than utilizing data from the FLGPR alone. We also analyze each sensor's incremental improvement of classification accuracy by performing numerous experiments with different permutations of the sensors.