The F-16 Automatic-Ground Collision Avoidance System (Auto-GCAS) has been a resounding success since implementation in Nov 2014, saving 8 pilots and 7aircraft from Controlled Flight into Terrain (CFIT). However, there is no implemented Auto-GCAS for heavy performance limited aircraft. This research endeavors to expand on the success of F-16 Auto-GCAS to other aircraft in the Air Force inventory such as the C-130, C-17, and B-1. MIL-STD-1797 classifies performance limited aircraft as large, heavy, and low to medium maneuverability. Using a stitched Learjet-25D model (LJ-25D), an Auto-GCAS algorithm was developed to predict multiple escape-maneuver trajectories, compare these paths to digital terrain elevation data (DTED), and trigger the most aggressive escape path upon predicted terrain collision. Multiple numerical integration methods were compared to balance computational speed vs. accuracy. The Adams-Bashforth multi-step method showed improved accuracy and increased speed than the previous Euler Explicit method. Simulations in a modified DTED terrain map evaluated differences in Auto-GCAS algorithm design, principally forward look-ahead time. Results showed extending the forward look-ahead time past 45 s did not decrease collision prevention, but changing the trigger activation to the forward-open method successfully reduced thenumber of collisions. An algorithm was developed to vary trajectory prediction based on airspeed and performance variables without increasing computational cost. This Trajectory Prediction Algorithm (TPA) was able to extend the forward climb look-ahead time approximately 20 seconds at slower airspeeds and successfully escape a box canyon where previous methods failed. Preliminary pilot feedback was collected through a piloted simulator study at Air ForceResearch Laboratory's (AFRL) Multi-Crew Cockpit Simulator (MCCS).
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