Improved Methods of Combat Identification
Abstract
Complex sensor networks are an enormously large and expensive set of systems, software, sensors, and emitters that generate data contributing to battlespace awareness and combat identification. Multiple sensor network configurations can collect so much data that an ineffective Data Rich, Information Poor (DRIP) situation results. The desired solution for actionable information is coordinating high-value units (HVUs) to support actions such as identification and targeting. Timely detection of unknown signals is currently inadequate to maintain situational awareness at a tactical level.Few analysts have the experience and data access required to make effective use of the available data. Those who do are not close enough to the edge to support warfightings tactical and operational levels. Automating the workflow and processes of well-seasoned analysts combined with new AI and modeling technologies would enable improved and more timely extraction of essential information from the data and support better situational awareness and tactical decision-making. The motivation for analysis of combat and target identification stems from the significant pressure usually applied to make rapid, effective, and informed decisions. The goal of target ID is to analyze the threat of a potential target in order to make informed decisions about engaging it. The benefits of leveraging these graph-based methodologies include greater situational awareness with automation tools to distill contextualized information at the tactical and operational levels.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2023
- Accession Number
- AD1214572
Entities
People
- Jessica Kimball
- Johnathan C. Mun
Organizations
- Naval Postgraduate School