Artificial Intelligence Real-Time Track Modeling and Simulation for Combat Systems
AREA(S): Information Systems
PROGRAM: PEO IWS 1.0, AEGIS Combat System Program Office
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Provide an Artificial Intelligence (AI) capability for target identification
and behavior-based predictive track vector generation combined with Real-time
Modeling and Simulation (M&S) based combat system targeting and tracking
management that fills gaps presented in a communication and/or sensor
The growing proliferation of unmanned air and surface vehicles poses a
potential tactical threat to our current naval platforms. This threat continues
to grow as the technology needed to develop such vehicles in vast numbers
becomes easier for both state and non-state actors. The subsequent increase in
the number of potential air and surface targets tracked and engaged in a
tactical environment, where both communications and sensor data acquisition may
be hampered by enemy activity, is of critical concern. The development of a
mechanism that will provide the AEGIS (and potentially the Future Surface
Combatant (FSC)) combat system with real-time M&S-based tracking updates to
“fill in the gaps” when operating in a communication and/or sensor challenged
environment is needed.
Non real-time (RT) M&S has been in use for a number of years within the
Department of Defense (DoD) community. To date, its use has been more or less
constrained within the analytical community and used to develop tactical engagement
models for validating combat and weapons systems design requirements, tactical
and strategic engagement modeling, and so forth. Recent advances in both AI
(e.g., Deep Learning techniques pioneered by Google “Tensorflow” library &
framework) and high-speed parallel computing architectures (such as the Nvidia
and AMD Graphical Processing Unit (GPU) subsystems) may now provide the ability
to execute M&S algorithms in a real-time environment. The potential of
melding real-time M&S algorithms with known target behavioral models
utilizing newly developed AI algorithms and techniques could yield significant
tactical advantages. Current combat system tracking management algorithms
utilize a simple linear predictive model based on last known position and velocity
vector to update track data in situations where real-time sensor data is
unavailable due to sensor failure or active sensor and/or communications
The RT on-the-fly track M&S agent (hereafter referred to as the RTS Agent)
proposed technology is intended to function within a future tactical
environment that may contain a large number of battlespace entities, all
operating in a communications- and/or sensor-challenged electromagnetic (EM)
environment. In such an environment, both organic and non-organic sensor data
updates may be intermittently delayed or completely unavailable for an
indeterminate period. Providing a capability in the combat system to estimate
track position, velocity, and so forth during these intermittent periods will
provide the commander with real-time modeling-based information. Such
information will be of significant benefit in determining the actual current
state of the battlespace. Additionally, an enhanced track picture will help
reduce decision-related stress and fatigue by reducing the operator’s need to
ponder over each track to determine its status, thus allowing for a potential
increase in the operator’s ability to handle extended duty time and an
associated reduction in manning, potentially improving affordability. The Navy
seeks an innovative RTS Agent capability within the AEGIS combat system. The
technology will be arbitrarily scalable to an indefinite number of
battlespace-tracked entities, enabled by an architectural framework that
leverages multiple parallel processing (in both hardware and software) of
simultaneous tracks. Hence the final capacity would be determined by the
parallel processing capacity of the hardware available at implementation. It
will be relatively self-contained such that it will require only software
running within its current host combat systems suite and be integral within the
AEGIS combat system to provide complete single-platform based capability and
have minimal to no impact on the performance of the combat system. It will also
provide a well-defined and documented Applications Program Interface (API)
allowing it to be easily ported to other combat systems architectures (i.e.,
SSDS and the FSC combat system) currently in the planning stage.
The RTS Agent architecture will be capable of creating estimated track vector
updates based on RT track simulation and AI techniques utilizing prior track
behavior and other data. This data will be gleaned from a combination of prior
track behavior, its AI-projected target, and the tracked entity’s known
capability. The latter will be determined from its estimated entity ID,
referenced against an entity ID/Capability database.
The RTS Agent architecture proposed for development will be capable of
presenting probability metrics for each potential predicted track (or group of
tracks) in real time to the operator, with the goal of providing a >50%
improvement in probability of successful target engagement when compared to the
performance of an operator track picture unassisted or unaugmented by the RTS Agent.
The developed RTS Agent architecture will be capable of coordinating its
simulated track data with the track data of other platforms and performing
multi-platform AI-assisted simulated track de-confliction, when such data is
The developed RTS Agent architecture will be capable of re-establishing
simulated track synchronization with real-world sensor derived track data when
such data again becomes available to the combat system either on an
intermittent (asynchronous) or continuous basis. Both the developed RTS Agent
architecture and any associated AI algorithms should be well documented, and
conform to open systems architectural principle and standards. Architectural
implementation attributes should include scalability, support of a well-documented
open-systems API to support future capability upgrades, and the ability to run
within the computing resources available within the AEGIS combat systems BL9
The Phase II effort will likely require secure access, and NAVSEA will process
the DD254 to support the contractor for personnel and facility certification
for secure access. The Phase I effort will not require access to classified
information. If need be, data of the same level of complexity as secured data
will be provided to support Phase I work.
Work produced in Phase II may become classified. Note: The prospective
contractor(s) must be U.S. Owned and Operated with no Foreign Influence as
defined by DoD 5220.22-M, National Industrial Security Program Operating
Manual, unless acceptable mitigating procedures can and have been be
implemented and approved by the Defense Security Service (DSS). The selected
contractor and/or subcontractor must be able to acquire and maintain a secret
level facility and Personnel Security Clearances, in order to perform on
advanced phases of this contract as set forth by DSS and NAVSEA in order to
gain access to classified information pertaining to the national defense of the
United States and its allies; this will be an inherent requirement. The selected
company will be required to safeguard classified material IAW DoD 5220.22-M
during the advance phases of this contract.
I: Design, develop, and deliver architecture for a RTS Agent. Demonstrate that
the concept shows that its proposed architecture framework and conceptual model
can feasibly meet the requirements and parameters set forth in the Description.
Establish feasibility through a study and/or use of a simulation-based
analysis. Develop a Phase II plan. The Phase I Option, if exercised, will
include the initial design specifications and capabilities description to build
a prototype solution in Phase II.
II: Design, develop, and deliver a prototype RTS Agent that will demonstrate
the capability to perform all parameters in the Description. Perform the
demonstration at a Land Based Test Site (LBTS), provided by the Government,
which represents an AEGIS BL9 or newer combat system environment and is capable
of simultaneously simulating two AEGIS test platforms to allow for the demonstration
of multi-platform simulated track de-confliction capabilities. Ensure that the
prototype is capable of demonstrating its implementation and integration into
the combat system environment. Prepare a Phase III development plan to
transition the technology for Navy combat systems and potential commercial use.
It is probable that the work under this effort will be classified under Phase
II (see Description section for details).
III DUAL USE APPLICATIONS: Support the Navy in transitioning the RTS Agent to
Navy use as a fully functional software agent incorporated into the AEGIS
combat system baseline modernization process. Integrate the RTS Agent into a
baseline definition, validation testing, and combat system certification.
This capability has potential for dual-use capability within the commercial Air
Traffic Control systems in situations when air traffic sensor data may be
delayed or missing due to sensor or communications equipment failure.
Vasudevan, Vijay. “Tensorflow: A system for Large-Scale Machine Learning.”
Usenix Association, USENIX OSDI 2016 Conference, 2 November 2016. https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf
Vasudevan, Vijay. “TensorFlow: Large-Scale Machine Learning on Heterogeneous
Distributed Systems.” Usenix Association, 2016. http://download.tensorflow.org/paper/whitepaper2015.pdf
Schmidhuber, Jürgen. “Deep Learning in Neural Networks: An Overview.” Neural
Networks Journal, Vol 61, January 2016. http://www.sciencedirect.com/science/article/pii/S0893608014002135
Schmidt, Douglas. “A Naval Perspective on Open-Systems Architecture.” Software
Engineering Institute, Carnegie Mellon University, 27 March 2017. https://insights.sei.cmu.edu/sei_blog/2016/07/a-naval-perspective-on-open-systems-architecture.html
Paquin, J. N. “The What, Where and Why of Real-Time Simulation.” IEEE, 3 April
Real-time on-the-fly track Modeling and Simulation; Deep Learning techniques;
Communications and/or sensor challenged electromagnetic (EM) environment;
Artificial Intelligence; Multi-platform AI-assisted Simulated Track
De-confliction; AI Agent-based Software Design
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