Winning Tactical Engagements in Contested Environments through C5ISRT Dominance
The Johns Hopkins Applied Physics Laboratory (APL) Precision Strike Mission Area envisions a 2030 battlespace in which physical domains (e.g., land, maritime, air, and space) and the information domain are heavily contested and strongly coupled in terms of effects and outcomes. Creating a decisive advantage in this battlespace involves building command, control, communications, computing, cyber, intelligence, surveillance, reconnaissance, and targeting (C5ISRT) systems that provide a more complete, clear, accurate, current, assured, and accessible operating picture than an adversary’s picture. To this end, this article proposes a new control and analytical framework that views a C5ISRT system as a cognitive dynamical system with a perception-action cycle that continually and collaboratively orchestrates its resources to optimize the situational awareness available for tactical decision-making. The article describes a vision for research and development in battlespace awareness control and anti-control to achieve continuous universal targeting with impunity. We refer to the resulting decisive advantage as C5ISRT dominance.
Revolutionizing the Art of Strike and Air Combat: Guest Editors’ Introduction
The United States’ 2018 National Defense Strategy emphasized the nation’s need to face the challenge of near-peer adversaries like China and Russia. In the event of hostilities with either nation, US and allied forces will have to fight from ever-increasing range, with high-speed platforms and weapons, and deploy more effective nonkinetic capabilities. The scale of operations will drive us to machine-based intelligence and augmentation to enable human decisions at the speed of tactical relevance. The development of capabilities that address the challenges associated with distant near-peer engagement requires deliberate and strategic investment in technology solutions. The Precision Strike Mission Area (PSMA) of the Johns Hopkins University Applied Physics Laboratory (APL) has focused its internal independent research resources, combined with its direct sponsored tasking, to innovate and mature capabilities associated with four strategic vectors: Continuous Universal Targeting, Control Red Perception, Air Dominance, and Resilient Time-Critical Strike. This article introduces the strategic vectors, and the articles within the issue, organized around these vectors, present selected advancements that PSMA staff members are actively making in these strategic areas.
Inside Back Cover: DART
DART, NASA’s Double Asteroid Redirection Test, is the world’s first planetary defense test mission. After a successful launch from Vandenberg Space Force Base on November 23, 2021, DART is heading for the small moonlet asteroid Dimorphos, which orbits a larger companion asteroid called Didymos, with plans to intentionally crash into the asteroid to slightly change its orbit. While neither asteroid poses a threat to Earth, DART’s kinetic impact will prove that a spacecraft can autonomously navigate to a target asteroid and intentionally collide with it. Then, using Earth-based telescopes to measure the effects of the impact on the asteroid system, the mission will enhance modeling and predictive capabilities to help us better prepare for an actual asteroid threat should one ever be discovered. APL manages the DART mission for NASA’s Planetary Defense Coordination Office as a project of the agency’s Planetary Missions Program Office. NASA provides support for the mission from several centers, including the Jet Propulsion Laboratory in Southern California, Goddard Space Flight Center in Greenbelt, Maryland, Johnson Space Center in Houston, Glenn Research Center in Cleveland, and Langley Research Center in Hampton, Virginia.
Labs of the Lab
The Johns Hopkins University Applied Physics Laboratory (APL) creates technologies and innovations that serve national priorities and expand the frontiers of science. By combining creativity and technical expertise with a culture of risk-taking—brought together in cutting-edge collaboration spaces, labs, and test facilities across its campuses—APL’s researchers tackle increasingly difficult challenges with impacts across multiple domains. This article, the first in a recurring series, highlights just a few of the specialized laboratories enabling APL staff members’ critical contributions to the Lab, its sponsors, and the nation.
APL Achievement Awards and Prizes: The Lab’s Top Inventions, Discoveries, and Accomplishments in 2019 and 2020
Every year, the Johns Hopkins University Applied Physics Laboratory (APL) honors the accomplishments of its staff members with an awards program. At its inception more than three decades ago, the program recognized staff members’ exceptional contributions to the scientific community via publication. Today’s program continues to recognize outstanding publications but has grown to include awards and prizes celebrating extraordinary achievements in both sponsored programs and internal research and development, efforts that exemplify APL’s focus on transformative innovations, and, most recently, significant contributions that promote a positive, diverse, and inclusive culture at the Laboratory. In 2020, the program underwent yet another change as the Lab pivoted from the formal in-person ceremony on APL’s campus in Laurel, Maryland, to a safe and fun virtual format during the COVID-19 pandemic. This article details the awards presented for achievements in 2019 and 2020.
Modern Neural Networks
Deep neural networks have been tremendously successful in many areas from speech and image recognition to genomics. This article explores and provides insight into modern neural network concepts and applications; it is based on a chapter in the textbook Advanced Signal Processing: A Concise Guide published by McGraw Hill Professional in August 2020.
Maximum Likelihood Reliability Estimation from Subsystem and Full-System Tests: Method Overview and Illustrative Examples
This article provides an overview and examples of a novel and practical method for estimating the reliability of a complex system, with confidence regions, based on a combination of full-system and subsystem (and/or component or other) tests. It is assumed that the system is composed of multiple processes, where the subsystems may be arranged in series, parallel, combination series/parallel, or other mode. Maximum likelihood estimation (MLE) is used to estimate the overall system reliability based on the fusion of system and subsystem test data. The method is illustrated on two real-world systems: an aircraft-missile system and a highly reliable low-pressure coolant injection system in a commercial nuclear-power reactor. The examples demonstrate the following properties of the method: (1) Increasing the number of full-system tests improves the confidence in the full-system reliability estimate. (2) Increasing the number of tests of one subsystem stabilizes the subsystem reliability estimate. (3) The likelihood function and optimization constraints can readily be modified to handle systems consisting of repeated components in a mixed series/parallel configuration. (4) A normal distribution approximation for computing confidence intervals is not always appropriate, especially for highly reliable systems. (5) Performing a mixture of full-system and subsystem tests is important when the model that relates the subsystem reliability to the full-system reliability is uncertain.
In Memoriam: Kenneth R. Moscati (1946–2021)
This article pays tribute to Kenneth Moscati, longtime senior illustrator for the Technical Digest, who died March 15, 2021. Ken made major contributions to APL’s technical illustrations, large environmental displays, and publications, particularly the Technical Digest.
APL’s Young Professionals Network Looks toward the Lab’s Centennial
This article and the illustrations that follow highlight what members of APL’s Young Professionals Network (YPN) think the Lab might look like when it reaches its centennial. YPN aims to assist early-career staff members build community and develop their careers, while giving them the opportunity to help shape the future of the Lab by providing input on APL leadership’s strategic planning directions. Today’s YPN members will be at the height of their careers—and possibly APL’s leaders—in 2042, so their insights are especially valuable.