Leveraging Artificial Intelligence and Automatic Target Recognition to Accelerate Deliberate Targeting
Abstract
Evidence demonstrates the deliberate targeting cycle lagged the pace of low-end conflict in Operation Inherent Resolve (OIR). If the joint force does not identify and mitigate the factors which led to this phenomenon, then these factors will certainly be amplified in high-end conflict. This research examines deliberate targeting in OIR, identifies causal factors, and recommends improvements to ensure advantage in future conflict. To secure victory in tomorrows war, the joint force must address the three prevailing causal factors of inadequate intelligence capacity, insufficient ISR asset availability, and redundant target vetting within the deliberate targeting cycle. Research into Artificial Intelligence (AI) and Automatic Target Recognition (ATR) illuminates various efforts having the potential to mitigate these deliberate targeting deficiencies. For one, machine learning, data mining, and data fusion are AI capabilities which could be applied to the intelligence challenges identified in the research. In addition, autonomous systems which find, fix, identify, and track targets are ATR capabilities which could augment deliberate targeting and enhance the JADC2 of forces. With these technologies in mind, several recommendations are offered with the overall objective of accelerating deliberate targeting through augmentation of the process with AI and ATR. These technologies have the potential to bolster intelligence capacity, improve ISR asset availability, and accelerate strike approval. In turn, these improvements could enhance JADC2 and ensure decision advantage in future conflict.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 28, 2020
- Accession Number
- AD1107486
Entities
People
- Gary P. Beckett
Organizations
- Air War College