The Navy wants to improve ship identification by fusing different types of sensors.
Radar, EO/IR, lasers and electronic support measures such as anti-radiation homing have different features for identifying ships. Yet "current technology approaches develop automatic target recognition systems for a single sensor, each designed to exploit the salient features specific to each sensor type, which leads to suboptimal classification performance for each sensor type and not a higher confidence performance by combining independent sensor data into a single solution," says the Navy Small Business Innovation Research proposal.
So the Navy wants to take advantage of machine learning to determine how different types of sensors can be fused for ship classification and identification.
"Recent advances in machine learning can be explored to discover and to fuse the different feature information inherent within the different sensor types while advances in mobile computing processors enables these machine learning approaches to work efficiently and robustly in real-time."
The Navy is especially interested in fusing Inverse Synthetic Aperture Radar images with infrared imaging.
Michael Peck is a correspondent for Defense News and a columnist for the Center for European Policy Analysis. He holds an M.A. in political science from Rutgers University. Find him on X at @Mipeck1. His email is mikedefense1@gmail.com.
