SBIR/STTR Award attributes
Polarization imaging has shown significant improvements in contrast in several target detection and discrimination applications. Infrared earth backgrounds can be very complex making it very difficult for a thermal passive sensor system to acquire and identify a target amongst background objects of similar color, size and temperature. Because of this difficulty, the Army has identified the need to enhance sensor performance in a wide variety of tactical scenarios such as detection of landmines and IEDs, and detection/identification of camouflaged/hidden targets for route clearance and reconnaissance. Polaris proposes to combine dual-band MWIR/LWIR thermal/polarization sensing which has demonstrated superior detection capabilities with Artificial Intelligence and Machine Learning (AI&ML) algorithms to produce a next generation reconnaissance and route clearance sensor that addresses the current technology gap. The AI&ML algorithms will include Deep Neural Net (DNN), Continuous Machine Learning (CML), and other Machine Learning and Artificial Intelligence algorithms to enhance the detection and classification capability of the multimode MWIR/LWIR thermal/polarization camera.
