Passively Augmented LIDAR

The Air Force’s rapid, effective decision-making, strategic capability relies on the ability to autonomously provide better information through data fusion and visualization of sensor output so that it can be quickly understood and acted upon.  The ability to detect and classify targets autonomously is vital to the success of many Intelligence, Surveillance and Reconnaissance (ISR) missions, guidance and control munitions and rockets, and other warfighter activities. Each sensing modality has good performance in some scenarios and poor performance in others.  Often times, sensing modalities can be combined in ways that complement one another to significantly increase effectiveness.  In this SBIR, Polaris Sensor Technologies (PST) proposes to combine three sensing modalities that complement one another such that the multi-modal sensor has the strengths of all three, but is not limited by the weaknesses of any individual sensor. The three sensing modalities are long-wave infrared (LWIR) thermal imaging, LWIR polarimetric imaging, and 1550nm eye-safe LiDAR 3D imaging.  The latest machine learning algorithms are trained for detection/classification using data from all sensing modalities to produce a multi-modal sensor that detects and classifies targets over a wide Field of View (FOV).  PST therefore proposes to combine the capability of Pyxis LWIR that is able to detect targets with a few pixels over a wide FOV, and has some capability of classifying targets, with a narrow FOV LiDAR that has shown excellent promise for classifying targets.  Since polarization is largely used to cue the LiDAR, the combined modality sensor will be referred to as PQLiDAR (Polarization cued LiDAR).