Considerations for Cross Domain / Mission Resource Allocation and Replanning

NPS NRP Technical ReportNaval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources.N2/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.

ARTIFICIAL INTELLIGENCE-ENABLED MULTI-MISSION RESOURCE ALLOCATION TACTICAL DECISION AID

The Department of Defense supports many military platforms that execute multiple missions simultaneously. Platforms such as watercraft, aircraft, and land convoys support multiple missions over domains such as air and missile defense, anti-submarine warfare, strike operations, fires in support of ground operations, intelligence sensing and reconnaissance. However, major challenges to the human decision-maker exist in allocating these multi-mission resources such as the growth in battle-tempo, scale, and complexity of available platforms. This capstone study seeks to apply systems engineering to analyze the multi-mission resource allocation (MMRA) problem set to further enable artificial intelligence (AI) and machine learning tools to aid human decision-makers for initial and dynamic re-planning. To approach this problem, the study characterizes inputs and outputs of a potential MMRA process, then analyzes the scalability and complexity across three unique use cases: directed energy convoy protection, aviation support, and a carrier strike group. The critical findings of these diverse use cases were then assessed for similarities and differences to further understand commonalities for a joint AI-enabled MMRA tool.Civilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Strategy Synthesis for Autonomous Agents Using PRISM

We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas

Modeling and characterization of traffic flow patterns and identification of airspace density for UTM application

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas represe ting safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management

Unmanned Systems Sentinel / 3 June 2016

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Update NPS / February 2012

NPS Distinguished Professor Releases Two Books on Diverse Subjects; NPS Acquires Two USVs, Unveils New Sea Web Lab; Army's Intellectual Center Commander Visits NP

Mastering the Patrol: a Look at the Importance of Training For American Pilots in the Battle of the Atlantic

This thesis studies the role of training in the Allied victory in the Battle of the Atlantic. I argue that training played a significant role in this decisive battle in World War II. I detail the different stages of training for both the Navy and Army Air Force

COUNTER-UXS ENERGY AND OPERATIONAL ANALYSIS

At present, there exists a prioritization of identifying novel and innovative approaches to managing the small Unmanned Aircraft Systems (sUAS) threat. The near-future sUAS threat to U.S. forces and infrastructure indicates that current Counter-UAS (C-UAS) capabilities and tactics, techniques, and procedures (TTPs) need to evolve to pace the threat. An alternative approach utilizes a networked squadron of unmanned aerial vehicles (UAVs) designed for sUAS threat interdiction. This approach leverages high performance and Size, Weight, and Power (SWaP) conformance to create less expensive, but more capable, C-UAS devices to augment existing capabilities. This capstone report documents efforts to develop C-UAS technologies to reduce energy consumption and collaterally disruptive signal footprint while maintaining operational effectiveness. This project utilized Model Based System Engineering (MBSE) techniques to explore and assess these technologies within a mission context. A Concept of Operations was developed to provide the C-UAS Operational Concept. Operational analysis led to development of operational scenarios to define the System of Systems (SoS) concept, operating conditions, and required system capabilities. Resource architecture was developed to define the functional behaviors and system performance characteristics for C-UAS technologies. Lastly, a modeling and simulation (M&S) tool was developed to evaluate mission scenarios for C-UAS.Outstanding ThesisCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

In Review Magazine / January 2012

In Review magazine, published quarterly, created to showcase the best of NPS

Walker Talker: Friday, August 6, 1943

The Friday, August 6, 1943 edition of the Walker Talker.https://scholars.fhsu.edu/walker_talker/1020/thumbnail.jp