Improve LADAR Image and Data System Processing with Multi-Sensor Fusion in Vertical Lift Visual Degraded Environments

Visually degraded environment conditions (VDE sometimes referred to as Degraded Visual Environments (DVE)) pose a threat to Navy and Marine Corps helicopter operations during landings and troop insertions or extractions.  During low-level flight, blinding dust clouds caused by rotor down-wash can cause the pilots to suddenly lose all visual cues. This loss of situational awareness (SA) creates significant risks from other aircraft and ground obstacles.  The result is a significant degradation of a pilot’s situational awareness that has become one of the leading causes of lost helicopters.  Since 1991, there have been over 230 cases of aircraft damage and/or injury due to unsuccessful take-offs or landings in a dust environment.  Helicopter brownout is a $100 Million per year problem for the U.S. Military in Afghanistan and Iraq. The Army cites brownout in three out of every four helicopter accidents there.  AOS is developing the Advanced LADAR Imagery Augmentation System (ALIAS) to improve the resolution of Ball Aerospace and Technologies Corp. (Ball) LADAR Assisted Brownout Solution (LABS) data that can see beyond obscurants.

Using flash LADAR data collected specifically for this project, AOS applied sensor fusion, micro-scanning and interpolation to realize a more than 4x improvement in image resolution.  This can translate into over 2x improvement in FOV.  By fusing the two sensors, we were able to use smaller features detected by the 2D sensor to align the consecutive frames of LADAR data to sub-pixel levels based on feature matching. This enabled the 4x resolution improvement using only random platform motion.  The methods we demonstrated are extensible to any 2D sensor, including obscurant penetrating 2D sensors that can allow alignment of seemingly blinded LADAR frames.  The alignment of these frames makes many of the averaging and filtering techniques we examined feasible.  The increased LADAR resolution gives a better view of ground slope and small obstacles such as fence posts, wires, or small animals.  The wider FOV allows the pilot to see more of the Landing Zone (LZ), including hazards or other helicopters hiding inside it.  Finally sensor-fusion-enabled obscurant filtering significantly enhances the ability of the pilot to detect hazards, slopes, or other helicopters.

These images show improvements that are possible using ALIAS algorithms for image alignment and filtering.  The left image is a frame of an LWIR video with simulated dust, the right hand image is an image processed with AOS algorithms to reveal an otherwise obscured tank.

We have proven the feasibility of ALIAS during phase I of this project.  In Phase II we will implement the ALIAS algorithms on a real-time platform and demonstrate the combined ALIAS LADAR system in a relevant environment.