Regressionally-Estimated, CDE-Optimized, Integrated Into Launch (RECOIL) Weaponeering

Abstract

Collateral Damage is a large concern for military operations. The use of weaponeering software attempts to mitigate effects on collateral concerns while maximizing effects on the target. Unfortunately, this software is not available during dynamic targeting, which is the majority of operations for the AC-130 and other Special Operations Forces (SOF) aircraft. Modeling munitions effects against targets and optimizing employment parameters for Precision-Guided Munitions (PGMs) enables real-time alleviation for collateral concerns. It also has the added effect of reserving surplus munitions for large scale combat operations. This paper outlines the implementation of weaponeering models onto the AC-130J gunship using regression estimation and gradient-boosted decision tree machine learning. The AGM-176 model achieved an average of 0.81 R2 across all armored target sets with a MAE of 0.041. The HFR9E model also achieved an average R2 of 0.81, with a MAE of .040. This shows each specific probability prediction has an average error of 4 percent, which is acceptable for in-flight weaponeering.