Naval Integration into Joint Data Strategies and Architectures in JADC2

NPS NRP Technical ReportAs Joint capabilities mature and shape into the Joint All Domain C2 Concept, Services, COCOMs and Coalition Partners will need to invest into efforts that would seamlessly integrate into Joint capabilities. The objective for the Navy is to study the options for Navy, including Naval Special Warfare Command under SOCOM, on how to integrate Navy's data strategy and architecture under the unifying JADC2 umbrella. The other objectives are to explore alternatives considered by the SOCOM and the Air Force, which are responsible for JADC2 Information Advantage and Digital Mission Command & Control. A major purpose of Joint, Services/COCOMs, agencies and Coalition Partners capabilities is to provide shared core of integrated canonical services for data, information, and knowledge with representations for vertical interoperability across all command levels and JADC2, lateral interoperability between Naval Service/COCOMs, and any combination of JADC2 constituents, agencies, and coalition partners. Our research plan is to explore available data strategy options by leveraging previous NRP work (NPS-20-N313-A). We will participate in emerging data strategy by Navy JADC2 project Overmatch. By working with MITRE our team will explore Air Force JADC2 data strategy implemented in ABMS DataOne component. Our goal is to find a seamless integration between Naval Data Strategy and data strategies behind JADC2 Information Advantage and Digital Mission Command & Control capabilities. Our plan includes studying Service-to-Service and Service-to-COCOM interoperability options required for Joint operations with a goal to minimize OODA's loop latency across sensing, situation discovery & monitoring, and knowledge understanding-for-planning, deciding, and acting. Our team realizes JADC2 requires virtual model allowing interoperability between subordinate C2 for services, agencies, and partner. Without such flexible 'joint' intersection organizational principal hierarchical structure it would be impossible to define necessary temporal and spatial fidelities for each level of organizational command required for implanting JADC2. Research deliverables will document the results of the exploration of Joint, COCOM, Agency and Partner Data Strategies approaches as JADC2 interoperability options to the emerging JADC2. We strive for standard JADC2 interface. Keywords: JADC2, ABMS, DataOne, Information Advantage, Digital Mission Command, IntegrationN2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Hierarchical Collective Agent Network (HCAN) for efficient 3 fusion and management of multiple networked sensors

Agent-based software systems and applications are constructed by integrating diverse sets of components that are intelligent, heterogeneous, distributed, and concurrent. This paper describes a multi-agent system to assure the operation efficiency and reliability in data fusion and management of a set of networked distributive sensors (NDS). We discuss the general concept and architecture of a Hierarchical Collective Agent Network (HCAN) and its functional components for learning and adaptive control of the NDS. Sophistication of a HCAN control environment and an anatomy of the agent modules for enabling intelligent data fusion and management are presented. An exemplar HCAN is configured to support dynamic data fusion and automated sensor management in a simulated distributive and collaborative military sensor network for Global Missile Defense (GMD) application

Robot Wars: US Empire and geopolitics in the robotic age

How will the robot age transform warfare? What geopolitical futures are being imagined by the US military? This article constructs a robotic futurology to examine these crucial questions. Its central concern is how robots – driven by leaps in artificial intelligence and swarming – are rewiring the spaces and logics of US empire, warfare, and geopolitics. The article begins by building a more-than-human geopolitics to de-center the role of humans in conflict and foreground a worldly understanding of robots. The article then analyzes the idea of US empire, before speculating upon how and why robots are materializing new forms of proxy war. A three-part examination of the shifting spaces of US empire then follows: (1) Swarm Wars explores the implications of miniaturized drone swarming; (2) Roboworld investigates how robots are changing US military basing strategy and producing new topological spaces of violence; and (3) The Autogenic Battle-Site reveals how autonomous robots will produce emergent, technologically event-ful sites of security and violence – revolutionizing the battlespace. The conclusion reflects on the rise of a robotic US empire and its consequences for democracy

Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques

Mixed reality (MR) is a key technology which promises to change the future of warfare. An MR hybrid of physical outdoor environments and virtual military training will enable engagements with long distance enemies, both real and simulated. To enable this technology, a large-scale 3D model of a physical environment must be maintained based on live sensor observations. 3D reconstruction algorithms should utilize the low cost and pervasiveness of video camera sensors, from both overhead and soldier-level perspectives. Mapping speed and 3D quality can be balanced to enable live MR training in dynamic environments. Given these requirements, we survey several 3D reconstruction algorithms for large-scale mapping for military applications given only live video. We measure 3D reconstruction performance from common structure from motion, visual-SLAM, and photogrammetry techniques. This includes the open source algorithms COLMAP, ORB-SLAM3, and NeRF using Instant-NGP. We utilize the autonomous driving academic benchmark KITTI, which includes both dashboard camera video and lidar produced 3D ground truth. With the KITTI data, our primary contribution is a quantitative evaluation of 3D reconstruction computational speed when considering live video.Comment: Accepted to 2022 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), 13 page

AI-AUGMENTED DECISION SUPPORT SYSTEMS: APPLICATION IN MARITIME DECISION MAKING UNDER CONDITIONS OF METOC UNCERTAINTY

The ability for a human to overlay information from disparate sensor systems or remote databases into a common operational picture can enhance rapid decision making and implementation in a complex environment. This thesis focuses on operational uncertainty as a function of meteorological and oceanographic (METOC) effects on maritime route planning. Using an existing decision support system (DSS) with artificial intelligence (AI) algorithms developed by New Jersey Institute of Technology and University of Connecticut, cognitive load and time to decision were assessed for users of an AI-augmented DSS, accounting for METOC conditions and their effects, and users of a baseline, 'as is,' DSS system. Scenario uncertainty for the user was presented in the relative number of Pareto-optimal routes from two locations. Key results were (a) users of an AI-augmented DSS with a simplified interface completed assigned tasks in significantly less time than users of an information-dense, complex-interface AI-augmented DSS; (b) users of simplified, AI-augmented DSS arrived at decisions with lower cognitive load than baseline DSS and complex-interface AI-augmented DSS users; and (c) users relied mainly on quantitative data presented in tabular form to make route decisions. The differences found in user performance and cognitive load between levels of AI augmentation and interface complexity serve as a starting point for further exploration into maximizing the potential of human-machine teaming.Office of Naval ResearchMajor, United States Marine CorpsApproved for public release. distribution is unlimite

Intelligent wargaming approach to increase course of action effectiveness in military operations

In this study, an intelligent wargaming approach is proposed to evaluate the effectiveness of a military operation plan in terms of operational success and survivability of the assets. The proposed application is developed based on classical military decision making and planning (MDMP) workflow for ease of implementation into the real-world applications. Contributions of this study are threefold; a) developing an intelligent wargaming approach to accelerate the course of action (COA) analysis step in the MDMP which leads creating more candidate COAs for a military operation, b) generating effective tactics against the opposite forces to increase operational success, and c) developing an efficient, visual wargame-based MDMP framework for future systems that require a small team of operators to supervise a network of automated agents. Several example engagement scenarios are performed to evaluate the system capabilities and results are given. Moreover, fleet composition issue for automated agents is investigated and the fleet composer algorithm with hyperparameter tuning architecture is proposed

ARTIFICIAL INTELLIGENCE AND HUMAN INTERACTION: HOW TO KEEP THE HUMAN IN THE LOOP

Army leaders are looking to procure and implement artificial intelligence (AI) technologies to solve a variety of problems and enhance existing capabilities across multiple portfolios. While there are benefits to implementing new technologies, including AI, there is often a major pitfall: the human factor as a user is consistently underrepresented. This disparity between how AI-enabled systems are being acquired and how they should be acquired is often related to a gap in the development of systems not aligning with Human Systems Integration (HSI) best practices. The design of systems that facilitate human-agent learning requires further guidance. We use data from the System for Award Management (SAM) along with discussions from subject-matter experts both in government and industry to capture how AI-enabled systems are currently being procured by the Army. The combined results of the team's methodology revealed that there are varying understandings across the Army of what an AI requirement is, and there are no obvious processes or specific AI acquisition guidelines that are universally followed when developing an AI requirement. It was also apparent that HSI was not always included in requirements as required by Army regulations. This disparity appeared to have three major root causes: immaturity of DOD Army guidance, shortcomings in AI-related training for acquisition personnel, and a negligence surrounding the incorporation of HSI elements into Army requirements.CRUSERCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyApproved for public release. Distribution is unlimited

An Operational Utility Assessment: Measuring the Effectiveness of the Joint Concept Technology Demonstration (JCTD), Joint Forces Protection Advance Security System (JFPASS)

Sponsored Report (for Acquisition Research Program)Planning modern military operations requires an accurate intelligence assessment of potential threats, combined with a detailed assessment of the physical theater of operations. This information can then be combined with equipment and manpower resources to set up a logistically supportable operation that mitigates as much of the enemy threat as possible. Given such a daunting challenge, military planners often turn to intelligent software agents to support their efforts. The success of the mission often hinges on the accuracy of these plans and the integrity of the security umbrella provided. The purpose of this project is to provide a comprehensive assessment of the Joint Forces Protection Advanced Security System (JFPASS) Joint Concept Technology Demonstration (JCTD) to better meet force-protection needs. It will also address the adaptability of this technology to an ever-changing enemy threat by the use of intelligent software. This project will collect and analyze data pertaining to the research, development, testing, and effectiveness of the JFPASS and develop an operational effectiveness model to quantify overall system performance.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors