This PDF file contains the front matter associated with SPIE Proceedings Volume 11753, including the Title Page, Copyright information, and Table of Contents.

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Industry has realized the benefit of better decision making, agility, and savings by embracing digital transformation. Seeing the realized benefits in industry, the United States Air Force decided to digitally transform to keep up with ever-increasing rates of technical performance advances to stay ahead of its adversaries. The Air Force is a large and complex organization with an acquisition system forged out of the World War II-era Defense Industrial Base with a Vietnam War era-budgeting and resource allocation system. Weapon system acquisition complexity continues to grow and the Air Force is turning to digital transformation to quicken the speed of delivering weapon system capability to the warfighter. The Air Force has created a Digital Campaign to tackle this digital transformation for its acquisition enterprise. This paper advocates a strategy of “think big, start small, and scale fast” for the acquisition enterprise to achieve this transformation and meet the needs of the warfighter.

A modular and open product line can enable a myriad of approaches to reduce cost and schedule, but there are interdependencies that must be understood to avoid unintended consequences. To achieve the benefits of MOSA, we must identify the critical relationships and make informed trades to optimize customer’s cost, schedule and performance goals over the system life cycle. Because interrelations are different for different technologies, maximizing system benefits requires trades be performed at a more granular level. This paper introduces a framework for use case development and trade studies, to be performed collaboratively between Industry and Government.

For decades the U.S. Department of Defense (DoD), has been trying to harness the power and rewards of Open Architectures (OA) to achieve flexible weapon systems with low costs of integration and rapid upgrade capabilities. OAs provide a method for rapidly applying cybersecurity to systems as part of standardization updates. Recently, the DoD has directly endorsed OAs as a game-changer for weapon system development and identified clear OA leaders. This sets the stage for a future generation of weapon systems that support rapid integration, modification, and upgrades to enable warfighter capabilities in the field. This paper presents the difference between open architecture and open systems, a short history of OAs in the DoD, discusses how the DoD has renewed its support of OA, and ideas to maintain the momentum.

Chaos Engineering is a way to break things in a controlled manner to assess system resilience. One of the pioneers of this concept is Netflix with its Chaos Monkey tool. Their tool was designed to automatically take down virtual machines that host their services to understand how their system reacts. This paper examines Chaos Engineering in middleware network services, as they are becoming an important technology of modular open systems to improve their resiliency. We introduce the Chaos Engineering concept through fault injection and network manipulation. We run experiments where we apply these techniques individually against a target application running on the middleware system and collect data to get an understanding of how the system responds. The collected data leads to a greater understanding of system operation, which provides actionable insight into increasing the resilience of the system and its applications. Our fault injection experiments demonstrate the ability to find specific faults that, when injected into specific application points, cause the entire application to crash into an unrecoverable state. Our network manipulation experiments can pinpoint specific network conditions that cause failures to our target application. The goal of our research is to develop methods for applying Chaos Engineering to open systems architecture as a way to improve the resiliency of such systems against natural and adversarial failure conditions, which will, in turn, lead to the development of more resilient military mission systems.

The Sensor Open Systems Architecture™ (SOSA) Consortium seeks to decrease costs and increase performance of large-scale, standalone sensor systems like multi-sensor Electro-Optical Infrared (EO/IR) or Radio Detection and Ranging (RADAR), typically hosted on manned aircraft platforms. However, the greatest benefit of the SOSA Reference Architecture—interoperability—could also fuel the future of small, expendable systems such as Unmanned Aerial Vehicles (UAVs) and wearables. Not surprisingly, a sensor architecture crafted for large systems can be a challenge to host on small or distributed hardware. This paper presents three examples of the appeal and challenges inherent in defining an open architecture for the world of small sensors by asking, “Where does that module live?” The first example presents the case of sensors on small UAVs; for this case, the SOSA Technical Standard defines classes of connectors, in the vein of SAE AS6169™A, as well as mechanical interfaces. Interoperability and standardized interfaces would make sensors for UAVs easier and cheaper to assemble and deploy. However, the current SOSA Components and Modules definitions can be awkward for small UAV sensors. For instance, a small sensor may not contain a processor for hosting Automatic Target Recognition (ATR) on the sensor, but instead the platform may contain that processor, hence distributing the SOSA Modules onto different hardware elements on and off the host platform. The second example presents the case of using VITA 74.0-complaint hardware (VNX) for wearable sensor applications. It is appealing to apply the SOSA Reference Architecture to this new paradigm, but this tiny form factor as a part of the Internet of Battlefield Things (IoBT) may require a new contextualization of the boundaries of a reference architecture that includes distributed composable hardware elements. The third and last example presents the case of a swarm of sensor-enabled UAVs. The swarming sensor concept encourages yet a further reconceptualization of the SOSA Reference Architecture boundaries and the SOSA Data Model. As an open architecture originally intended for standalone systems and sensors, the SOSA Reference Architecture naturally assumes a sensor communicates primarily to the platform and never directly to other sensors. However, in order for a swarm of UAV-based sensors to reach maximum effectivity, the SOSA Reference Architecture could potentially extend its scope to include a swarm class with SOSA Modules that might not live either on the platform nor in a single sensor—they could be shared entities or organized in a hierarchy. These three examples illustrate how a forward-looking sensor open architecture can evolve to drive the future of smaller applications by expanding into these novel realms to provide a smart, flexible, small-form factor architecture.

Game-theoretic analysis allows for systematic analysis of complex adversarial situations modelled as extensive form games. Counterfactual regret minimization, the leading game-theoretic framework used to solve extensive form games, is generally used to develop improved and unexploitable decision agents. This paper focuses on the third-party planner – an agent with a stake in the outcome of a game yet have no agency within the game. Third-party planners include game organizers trying to anticipate game situations for broadcast and planning, those providing logistical support to the players, or anyone else who might interact with the game environment. While projecting game flow might in general be quite difficult, the problem becomes tractable if the players are sufficiently sophisticated as to follow an approximate equilibrium solution. This paper demonstrates how counterfactual regret minimization can assist third party planners under these circumstances.

Distributed beamforming (DBF) schemes are receiving increased interest for military and commercial applications due to radio frequency spectral congestion, the possibility of system implementation in autonomous systems, reduced interference requirements to existing legacy systems and/or other co-site signals, and the desire for improvements in low probability of intercept (LPI) and low probability of detection (LPD) transmissions. In this work, it is assumed that distributed beamforming is composed of distributed and collaborative beamforming nodes such that a beamforming gain can be achieved either with or without feedback between transmitter and receiver nodes. Open-loop DBF produces coherent beamforming gain either from a set of collaborating distributed transmitters and/or from a set of collaborating distributed receivers, where no feedback channel is required or available between the DBF transmitters and DBF receivers. Closed-loop DBF produces coherent beamforming gain from both the DBF transmitters and DBF receivers, but assumes a feedback channel exists between the transmitters and receivers. This work develops and demonstrates a method that can reach the maximum theoretical beamforming gain available in open-loop and closed-loop systems while each set of distributed nodes experiences non-ideal geometric array variation and synchronization offsets between distributed elements. Beamforming gain degradation is shown for mobile channel velocity variation. This work should provide useful application to a wide array of distributed autonomous systems as well as future 5G commercial applications.

Five algorithms are implemented for coverage control techniques for decentralized swarms of autonomous agents. Some of the algorithms are used for covering an area in general while others are intended for covering an area with an underlying priority density function. Standard Lloyd’s algorithm and Basic Weighted Lloyd’s algorithm are used as baselines in the analysis against three new algorithms: Even Distribution, Biased Weighted Lloyd’s, and Evolutionary. Experimental results demonstrate that each of these new algorithms improve the quality of the agents’ performance by either reducing coverage cost or distance travelled and/or time spent settling into an equilibrium formation.