Heterogeneous Multisensor Buried Target Detection Using Spatiotemporal Feature Learning


Timothy Havens, PI, William and Gloria Jackson Associate Professor of Computer Systems

Timothy Schulz, Co-PI, University Professor, Electrical and Computer Engineering

Sponsor: U.S. Army Research Office

Amount of Support: $285,900 (for the first year out of a potential 3-year project totaling $983,124)

Abstract: This project will investigate theory and algorithms for multisensor buried target detection that achieve high probability of detection and classification with low false-alarm-rate. The primary sensors of interest are multisensor FLGPR (i.e., FLGPR plus other sensor modalities, such as thermal video or LIDAR) and acoustic/seismic systems, although our methods will be applicable to other modalities as well.