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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.

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.

The ability to detect dark ships at open-ocean scale requires enhanced space-based intelligence, surveillance, and reconnaissance capabilities. With the boom of commercial space-based sensing, the nation needs an automated process to meet the growing volume and velocity of data. Multimodal data from the variety of existing and proposed space-based sensor networks can be aggregated and fused to produce target-quality tracks on ships. These sensor modalities include synthetic aperture radar (SAR), electro-optical/infrared (EO/IR), and Automatic Identification System (AIS). In this article, we demonstrate the work of a Johns Hopkins University Applied Physics Laboratory (APL) team to automate recognition of target surface vessels from these modalities on a next-generation spaceflight processor to simulate on-orbit detection. These detections can be fused to form quality tracks that can then be used to detect dark ship anomalies via pattern-of-life analysis. Tracks formed over a continental or global scale motivate the need for further automated analysis since a significant amount of human effort would be needed to analyze thousands or tens of thousands of tracks in detail and in real time. To address this challenge, the APL team developed a suite of pattern-of-life tools that extract features from tracks and flag tracks that deviate too far from some learned definition of normality.

Combat identification (CID) is the process of accurately characterizing battlespace entities to enable high-confidence, real-time application of tactical options, such as engagement. Evidence to support CID estimates is often sparse, latent in the battlefield, or both, raising the risk of association ambiguity and potential loss of CID custody. Therefore, an automated CID estimation methodology must properly account for and convey its results’ uncertainty, ambiguity, and ignorance to the warfighter to support timely, well-informed decision-making. The automated CID estimation process presented in this article is a computationally scalable approach to achieve robust CID custody in over-the-horizon targeting applications. Novel aspects of this approach include (1) a compact representation of track histories as tracking segments (vice measurements); (2) a temporal history of kinematic ambiguities between tracks; and (3) a transferable belief model for open-world evidential reasoning under uncertainty, ambiguity, and conflict. The result is an actionable, informative CID estimation process that accounts for real-world challenges and constraints.

Today’s electronic warfare (EW) missions face increasingly agile, multimodal, highly integrated, and long-range threats. To help its sponsors accomplish their missions in the face of these threats, the Johns Hopkins University Applied Physics Laboratory (APL) Precision Strike Mission Area developed a vision for achieving information dominance and delivering overwhelming effects against our adversaries. This vision relies on using our EW systems in concert with other operational platforms and capabilities to control adversary, or Red, perception. Implementing this strategy requires revolutionary advancements in EW systems so that they operate in an intelligent, distributed, and collaborative manner. Investment in foundational technologies that enable these capabilities is a prerequisite to accomplishing the strategy and staying ahead of pacing threats. This article describes the technology gaps that must be filled to realize the vision of controlling Red perception and details recent APL independent research and development projects that are positioned to provide game-changing thought leadership and capability innovations to satisfy those gaps.

Electronic warfare (EW) operators face a multitude of challenges when performing single- and distributed-platform sensing and jamming tasks in increasingly dense and agile threat environments. During an engagement timeline, actions often must be taken quickly and based on the partial information available. Recently, the world has observed a boom in artificial intelligence, a suite of data-driven lateral technologies that has already disrupted multiple fields where autonomy and big data are key elements. Although it is not the solution to all EW tasks, artificial intelligence shows promise in offering potential solutions to improve EW efficiency and effectiveness through informed decision-making beyond the capability of a human operator. The Johns Hopkins University Applied Physics Laboratory (APL) Precision Strike Mission Area has invested in research and development in the specific EW tasks of emitter identification and autonomous resource allocation. This article presents promising results from these projects and describes recommended future work in these areas, as well as additional EW applications that may benefit from research in artificial intelligence.

Distributed electronic attack and electronic support systems interact to complete a set of tasks and are of interest to the electronic warfare (EW) community. With the expanding operational threat space, the increasing complexity of emerging targets, and the increasing density of the electromagnetic environment, individual EW systems do not have sufficient resources to meet mission requirements. Moreover, current approaches to improve EW system interoperability and ensure Blue force communications constrain EW technique design and do not scale against emerging and future threats. Distributed and collaborative EW concepts offer potential relief to EW resource constraints by distributing sensing, communication, and engagement task management across multiple EW systems. While this vision offers many opportunities, its realization is currently limited by science and technology (S&T) gaps and incomplete functional requirements that prevent the precise definition of a distributed EW resource manager. In this article, we describe distributed EW use cases and associated functional requirements to motivate the need for a distributed resource manager architecture, and we identify the distributed resources to be managed. For future work, we suggest key focus areas and enabling technologies that can bridge the S&T gaps for the design of EW resource management.

The Defense Advanced Research Projects Agency (DARPA) Air Combat Evolution (ACE) program “seeks to increase trust in combat autonomy by using human–machine collaborative dogfighting as its challenge problem. This also serves as an entry point into complex human–machine collaboration” (https://www.darpa.mil/program/air-combat-evolution). To set the stage for ACE, the AlphaDogfight Trials program was created to explore whether artificial intelligence (AI) agents could effectively learn basic fighter maneuvers. DARPA contracted the Johns Hopkins University Applied Physics Laboratory (APL) to create an arena to host simulated dogfights— close-range aerial battles between fighter aircraft—where autonomous agents could be trained to defeat adversary aircraft. During the dogfight trials, AI agents competed against each other and the winner competed against a human pilot. By the end of the trials, the program demonstrated that AI agents could surpass the performance of human experts. APL was critical to the success of this program: the Lab created the simulation infrastructure, developed the adversary AI agents, and evaluated the competitors’ AI solutions. This article details APL’s role in advancing combat autonomy through this program.

The holistic assessment of any combat system is incomplete without evaluation of the human component. The human operator is a key, perhaps the key, component of successful combat operations in complex environments. The Naval Air Systems Command (NAVAIR) recognized the need to consider aircrew proficiency in the achievement of warfighting objectives. In response, the Johns Hopkins University Applied Physics Laboratory (APL) developed the Proficiency-Enabled Mission Model (PEMM) to characterize the impact of operator training and readiness on mission effectiveness in the context of strike-fighter aircraft in air combat. APL’s development of PEMM has advanced the state of the art for air combat modeling and simulation by introducing aircrew proficiency while executing current tactics, techniques, and procedures in the Brawler combat simulation environment. The F/A-18E/F Super Hornet defensive counter-air mission served as the initial case for proof of concept. The resulting capability informed investment decisions and training enhancements for that community. This article facilitates extension of this methodology by summarizing the process for producing a data-driven proficiency-enabled mission model with specific attention to tactics encoding, data collection, and simulation environment prerequisites.

Modern warfare demands situational awareness of entities in the environment. To enhance the warfighter’s situational awareness, we developed an algorithm that detects anomalous behavior in the warfare environment. Changes in entities’ behavior can be an indicator that existing prediction models or assumptions must be updated to remain useful for decision-making. Specifically, we introduce a new classification method—sequential sample consensus (SeqSAC)—that identifies anomalous behavior based on a series of observations of an entity. SeqSAC can support a wide variety of models from simple to complex. We first demonstrate the utility of SeqSAC with a simple limited-degree-of-freedom kinematic model of a moving body, and then we demonstrate the ability to incorporate more complex models using the finite-state machine in Advanced Framework for Simulation, Integration and Modeling (AFSIM). Finally, we discuss the ability to extend SeqSAC to identify anomalies in coordinated entity behaviors.

The Johns Hopkins University Applied Physics Laboratory (APL) explored a reusable hybrid rocket design to enable low-cost, rapid flight testing. Rocket motor reusability requires addressing the unique thermal challenges of the combustion chamber. Specifically, APL focused on addressing an unexpected thermal load on the forward bulkhead that resulted in melted aluminum near the injector. Thermal management design concepts included changes to the forward bulkhead by adding insulation, lengthening the precombustion chamber, and adjusting the spray angle of the injector. The design study showed that both lengthening the precombustion chamber and using an axial injector with contoured ports resulted in adequate thermal management, confirming that aluminum is suitable for the hybrid rocket combustion chamber forward bulkhead in APL’s design.

One of the fundamental challenges of fielding and maneuvering a hypersonic vehicle is predicting the large changes in heat transfer and aerodynamic performance associated with the transition of the surface boundary-layer flow from laminar to turbulent during flight. Legacy methods for analyzing boundary-layer transition are overly simplistic and do not account for the intricate flow patterns of modern vehicles with complex three-dimensional shapes. This article introduces work utilizing a novel methodology, known as input/output (I/O) analysis, recently applied to hypersonic flows. This methodology is completely free of geometric constraints and has significant potential to answer many of the open questions in transition analysis. The article presents examples of I/O analysis applied to hypersonic flow over a 7° half-angle sharp cone and to the Boundary Layer Transition (BOLT) flight experiment. The analysis uses computational tools that were built in collaboration with the University of Minnesota and VirtusAero as part of a Johns Hopkins University Applied Physics Laboratory (APL) independent research and development project.

The Boundary Layer Transition (BOLT) flight experiment, a unique collaboration spanning academia, government, and industry, sought to obtain flight data on a critical phenomenon affecting hypersonic vehicle design. The project aimed to further understanding of the physics of boundary-layer laminar-turbulent transition on a complex geometry, a process that can significantly increase heating and can affect hypersonic vehicle drag, controllability, and engine performance. The Johns Hopkins University Applied Physics Laboratory (APL), the project’s principal investigator, led a large team of external collaborators to design a sounding rocket flight experiment over an 18-month period, while conducting an extensive campaign of wind-tunnel experiments and computational simulations to predict the flow physics on the BOLT geometry. The final flight experiment was built and instrumented at APL using Laboratory expertise in designing and prototyping hardware for extreme environments. The BOLT experiment was delivered to the US Air Force for the flight experiment, designed to gather critical validation data on BOLT’s boundary-layer transition from over 340 sensors in the hypersonic flight regime. Although the flight test ultimately did not achieve the desired experimental conditions, the BOLT research resulted in new experimental databases, new computational tool development for complicated hypersonic flows, and significant new workforce development through the inclusion of students in the program. APL’s efforts to develop BOLT led to a follow-on flight experiment focused on turbulence (BOLT2: The Holden Mission), which flew successfully in March 2022.

Scott T. Radcliffe, a chief scientist at APL, died February 3, 2022, in Howard County General Hospital at the age of 55 due to complications from bone marrow cancer and lung disease. He was known by his coworkers as “a national treasure, and a man of honesty and integrity who could imagine great solutions to things that nobody else could see.” He held three patents and received numerous awards from government sponsors and APL, including the Lab’s esteemed Alvin R. Eaton Award for sustained performance and exceptional scientific or engineering innovations. Scott had deep expertise in computer simulation, digital signal processing, and radio communications, and he delivered several game-changing communication, radio frequency (RF) sensing, and RF geolocation innovations for the U S government. He formed deep relationships with sponsors and colleagues, some of whom contributed personal remembrances in this tribute to him.

This illustration is a notional mission-level view of the four APL Precision Strike Mission Area (PSMA) strategic vectors working together. Continuous Universal Targeting is illustrated as sensors observing Red targets and relaying targeting information to Blue tactical platforms. Control Red Perception is illustrated as Blue airborne and surface ship jamming platforms achieving nonkinetic effects against Red airborne and surface targets. Air Dominance is illustrated as crewed and uncrewed airborne platforms working together to maintain air supremacy. Resilient Time- Critical Strike is illustrated as Blue hypersonic weapons attacking Red land and sea targets.