Target Identification Interrogation Data Stream Analytics System
AREA(S): Information Systems
PROGRAM: PEO IWS 1.0, AEGIS Combat System Program Office
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Develop a system architecture and algorithmic framework to identify air targets
in real time quickly, accurately, and reliably for the AEGIS Combat System.
Data stream analytics have been used in industry for a number of years to solve
various logistical and other problems using on-the-fly monitoring, data-mining,
and analysis of ongoing information streams. Recent advances in both Artificial
Intelligence (AI) (e.g., Deep Learning techniques pioneered by Google) and
high-speed parallel computing architectures (such as the Nvidia and AMD
Graphical Processing Unit (GPU) subsystems) may now provide the ability to
execute such data-stream analysis algorithms in real time.
Current Combat System Track Identification (ID) methodology utilizes
transponder-based track ID data, Radio Frequency (RF) and voice interrogation
of the potential air track under investigation, and estimated ID based on the
operator’s best judgement when no other viable source of ID is available. In an
environment where tactical communications are challenged or denied (e.g., where
voice communications and/or transponder ID data may be unavailable), the
operator is forced to rely only on his/her knowledge of the Area of
Responsibility (AOR), the current Tactical Situation (TACSIT), and his/her own
experience in determining if an air track is a potential threat. A system
capable of providing the operator with additional ID options analytically
derived from the observed air track behavior, with each potential ID suggestion
ranked by probability, will greatly assist the operator in making a final track
ID assignment to a questionable air track. An enhanced and semi-automated track
ID capability will also contribute to the reduction of operator fatigue by reducing
the operator’s need to ponder over each track ID to determine its veracity,
thus allowing for a potential increase in the operator’s ability to handle
extended duty time resulting in an associated reduction in manning by more than
20%, and improving affordability. One of the principal goals of this effort is
to improve the operating efficiency of the combat systems air track
identification capability, allowing a significant (>50%) improvement in the
probability of successful identification for any specific track in the
communications/sensor denied environment mentioned above. The current air
traffic control aircraft ID uses mode-S transponder data stream provided by the
aircraft. The issue is that mode-S is not a secure/verifiable source -
transponders in aircraft can be switched/exchanged or modified. An
alternate/verifiable form of aircraft identification needs to be developed that
does not necessarily rely on the cooperation of the aircraft.
The Navy seeks a software system architecture and algorithmic model that
implements real-time target track ID assignment within the AEGIS combat system.
The system model architectural attributes will include scalability to process
(in parallel) a large number (i.e., on the order of 10 times the current AEGIS
capacity) of air tracks within the Common Operational Picture (COP). The system
needs to be self-contained (i.e., require only software running within its
current host combat systems suite to provide complete single-platform based
capability) and have minimal impact on the performance of the current combat
system. The system must also provide a well-defined and documented Applications
Program Interface (API) allowing portability of the architecture and algorithms
to other combat systems (e.g., Ship Self Defense System (SSDS) and the Future
Surface Combatant (FSC) combat system).
The proposed system architecture and associated analytic algorithms must be
capable of generating a real-time track ID for all air tracks within the COP
based on an analytical combination of available parameters. These parameters
may include a prospective air target transponder provided ID, observed
real-time track behavioral characteristics (air speed, maneuver radius,
projected destination, radar signature analysis, etc.) analyzed against a known
track airframe dataset comprised of previously collected air track data and
behavioral data retrieved from a shipboard airframe track database, and current
principal ship TACSIT and geographic location with respect to known commercial
air-traffic patterns in the AOR. The system must be capable of generating
alterative track IDs developed in real time as a set of probability-ranked
options. Each option will have associated track-ID reliability metrics that
will indicate its relative merit with respect to the other options presented.
The proposed system will allow an operator to specify an ID reliability
threshold after which the real-time analysis will provide the alternate ID
suggestions. The technology will also utilize multi-platform sourced data
streams (when available) to provide a multi-platform distributed track ID
capability and improve the reliability of its ID recommendations. The value of
having a set of continuously updated probability ranked air track ID options
available to the console operator will greatly reduce the probability of
incorrect air track identification that could result in either erroneous
engagement of non-threatening tracks, or non-engagement of lethal threats.
The proposed system must rapidly present the initial analytical results for
real-time use in the operator’s decision-making process. However, the
analytical process should not complete once the initial results are presented.
The technology will continuously and dynamically update and present enhanced
analytical results (i.e., updated target ID recommendations) in a real-time
manner as the tactical air track data stream evolves. The method used to
present the analytical results must be compatible with and implementable within
the currently implemented AEGIS software and hardware display infrastructure.
The technology will be well documented and conform to open systems
architectural principals and standards.
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 will likely 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: Develop a concept for a software architecture and real-time data-stream
analytics system as identified in the Description. Demonstrate that the model
will show that it can feasibly meet the requirements in the Description.
Establish feasibility through evaluation of the proposed model via 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 required to build a prototype in Phase II.
II: Develop and deliver a prototype software architecture and real-time
data-stream analytics system that demonstrates the capability to perform all
parameters described in the Description after implementation and integration
into the combat system environment. Perform the demonstration at a Land Based
Test Site (LBTS), provided by the Government, that represents an AEGIS BL9 or
newer combat system environment and that should be capable of simultaneously
simulating two AEGIS test platforms, to allow for the demonstration of track ID
generation using sensor data provided by two cooperating platforms. Ensure that
the prototype will demonstrate it has little to no impact on the performance of
the combat system environment. The company will prepare a Phase III development
plan to transition the technology for Navy combat systems and potential
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 technology to
Navy use. Implement a fully functional software architecture and real-time
data-stream analytics algorithms system into the AEGIS combat system baseline
modernization process, consisting of integrating into the combat system
baseline, validation testing, and combat system certification.
This architecture can benefit the commercial air traffic control systems,
providing a potential capability to identify unknown air tracks utilizing
commercial air space and/or approaching civilian airports. Such a capability
may prove useful in civilian anti-terrorism scenarios.
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, Volume 61, January 2015, pp. 85-117. http://www.sciencedirect.com/science/article/pii/S0893608014002135
Schmidt, Douglas. “A Naval Perspective on Open-Systems Architecture.” Carnegie
Mellon University, Software Engineering Institute, SEI Blog, Posted 11 July
Real-time Target Track ID; Deep Learning Techniques; Transponder Identification;
ID Spoofing; Artificial Intelligence; Multi-platform Track ID; Track-ID
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