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Methodology for Improving the Planning, Execution, and Assessment of Intelligence, Surveillance, and Reconnaissance Operations
Lingel et al. present alternative methods to (1) approach U.S. Air Force intelligence, surveillance, and reconnaissance (ISR) tasking and assessment processes and (2) outline a methodology for assessing the benefits and costs of different ISR employment strategies. The U.S. Air Force greatly increased the number of operational surveillance and reconnaissance sensors and its ability to process data from these sensors in support of operations across a wide range of conflicts. However, along with the increased number of sensors comes an increase in the complexity of the tasking of these assets needed to prosecute either planned for or emergent battlefield targets. As part of the authors' research, they developed new assessment techniques and operational strategies to improve the command and control process for assigning ISR assets in dynamic environments. The authors also suggest tools to assist commanders of ISR assets in their decisions regarding allocating and retasking ISR assets. The report focuses on traditional target sets against adversaries whose behavior is well understood.
Mastering AI
A Fortune magazine journalist draws on his expertise and extensive contacts among the companies and scientists at the forefront of artificial intelligence to offer dramatic predictions of AI’s impact over the next decade, from reshaping our economy and the way we work, learn, and create to unknitting our social fabric, jeopardizing our democracy, and fundamentally altering the way we think. Within the next five years, Jeremy Kahn predicts, AI will disrupt almost every industry and enterprise, with vastly increased efficiency and productivity. It will restructure the workforce, making AI copilots a must for every knowledge worker. It will revamp education, meaning children around the world ...
A RAND Analysis Tool for Intelligence, Surveillance, and Reconnaissance
The RAND Corporation's Collection Operations Model (COM) is a stochastic, agent-based simulation tool designed to support the analysis of command, control, communications, intelligence, surveillance, and reconnaissance (C3ISR) processes and scenarios. Written for the System Effectiveness Analysis Simulation modeling environment, the COM is used to study processes that require the real-time interaction of many players and to answer questions about force mix, system effectiveness, concepts of operations, basing and logistics, and capability-based assessment. It can represent thousands of autonomous, interacting platforms and explore the capabilities of a wide range of intelligence, surveillanc...
A Wargame Method for Assessing Risk and Resilience of Military Command-and-control Organizations
Many recent wargames conducted by the joint community have examined alternative organizational structures for operational-level command and control (C2) in a scenario. However, wargames typically role-play and exercise the roles, responsibilities, and authorities of a prescribed C2 organizational structure in the scenario rather than compare and contrast alternative structures in a rigorous manner. This report provides a "how-to" guide for conducting a C2 risk and resilience (C2R2) tabletop exercise (TTX). The C2R2 TTX is a wargaming method that RAND researchers developed and can be used to compare and contrast alternative organizational structures for operational-level C2 in terms of associ...
Technology Innovation and the Future of Air Force Intelligence Analysis
Intelligence collections and demand have grown over the past two decades, and intelligence analysts are often performing routine tasks, leaving them unable to conduct larger strategic analyses that are needed to address future threats as outlined by the 2018 National Defense Strategy. The authors provide an in-depth analysis of technologies that could help the Air Force Distributed Common Ground System (AF DCGS) become more effective, efficient, adept at using human capital, and agile. A key point is that artificial intelligence (AI) and machine learning (ML) technologies alone do not solve these intelligence challenges; rather, if they are properly implemented and complemented by human anal...
Technology Innovation and the Future of Air Force Intelligence Analysis
There is growing demand for the Air Force Distributed Common Ground System (AF DCGS) to analyze sensor data. The authors assessed how new tools and technologies, including artificial intelligence and machine learning (AI/ML), can help meet these demands. The authors assessed AF DCGS tools and processes, surveyed the state of the art in AI/ML methods, and examined best practices to encourage innovation and to incorporate new tools.
Data-Enabled Approaches for Enhancing the Air Force Transformational Capability Pipeline
To aid the Air Force Transformational Capabilities Office, the authors of the report developed a data science tool to extract information from free-text descriptions. They demonstrate the tool and foresight methods in three case studies.
Technology Innovation and the Future of Air Force Intelligence Analysis
The authors recommend tools, technologies, and processes to address the growing demand for Air Force Distributed Common Ground System support as it relates to intelligence squadrons within the 480th Intelligence, Surveillance and Reconnaissance Wing.
