Big Data Tools for High-speed Threat Detection and Classification
AREA(S): Information Systems
PROGRAM: Program Executive Office Integrated Warfare System (PEO IWS) 5.0 –
technology within this topic is restricted under the International Traffic in
Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and
import of defense-related material and services, including export of sensitive
technical data, or the Export Administration Regulation (EAR), 15 CFR Parts
730-774, which controls dual use items. Offerors must disclose any proposed use
of foreign nationals (FNs), their country(ies) of origin, the type of visa or
work permit possessed, and the statement of work (SOW) tasks intended for
accomplishment by the FN(s) in accordance with section 3.5 of the Announcement.
Offerors are advised foreign nationals proposed to perform on this topic may be
restricted due to the technical data under US Export Control Laws.
Develop innovative big data analysis tools to detect, classify, and localize
acoustic signals from torpedo-like targets.
Moderate to high interference sources such as merchant ships and biologics
often obscure threat signals evident on sonar display surfaces. In these
situations, automated detection and classification of targets is especially
challenging. However, an operator can typically identify potential threats
amidst surrounding interferers if they focus on the bearing of the target. This
potentially introduces unacceptable delays in operators’ abilities to identify
the bearing of that potential threat. The most time-critical threats are
threats from torpedoes and rogue surface craft. The current level of automation
predominantly trains on submarines and U.S. exercise torpedoes. Automation that
detects torpedo-like threats needs to be optimized to remove delays in
identifying these threats in moderate to high interference situations.
Deep learning, automated machine learning (ML), and big-data techniques have
facilitated voice and facial recognition technology in devices such as cell
phones, home security systems, and surveillance systems. The techniques used in
these devices require massive amounts of data for training and testing an
algorithm, the basis for computer metric analysis. While significant
computational resources are required to process the data during the algorithm
development phase, the resultant algorithm is fairly lightweight and portable.
Modern sonar systems generate massive amounts of data. For example, the
AN/SQQ-89 A(V)15 Undersea Warfare Combat System creates several hundred
surfaces for automation and operator interrogation.
The Navy seeks development of innovative tools that provide timely and accurate
detection, classification, and localization of threat targets; improves
operator proficiency; and reduces the detect-to-engage (DTE) timeline. Through
the use of big data analysis tools, the Navy seeks to expand current capability
to better detect rest of world (ROW) threats and generically exploit passive
acoustic characteristics present in all torpedo-like threats. An innovative
approach is needed that will apply deep learning, ML, and big data techniques
to acoustic and/or display-ready surface data to identify and localize threat
targets in the data.
In Phase I, the developer will use representative, open source, Waveform Audio
(WAV) files containing in-water interference sources (e.g. shipping noise,
biologics, etc.) and a target of interest (e.g. high speed motor boat ) for
which locations and identification of both interfering sources and the target
of interest are known will be used to determine technology performance. The
technology developed will be incorporated into the existing digital signal
processing chain to support a high probability of correct classification and a
low false alert rate to support existing operator displays. The technology
should be capable of at least 70% correct classification with a false alert
rate of no more than one (1) per hour in a semi-cluttered environment (e.g., a
combatant in the presence of two surface vessels, two or more bathymetric
features, and one target). Achieving a false alert rate of no more than one (1)
per hour is especially important and will be a key metric in performance
assessment. Transitions of these solutions to a tactical baseline will improve
overall ship survivability in mission-critical situations. Initial technology
transition is targeting the AN/SQQ-89A(V)15 Advanced Capability Builds (ACB)
for U.S. combatants and other platforms performing Anti-Submarine Warfare (ASW)
tasking. Therefore, it is important that the capability be feasible to
integrate with a tactical sonar system. Open source WAV files will be used to
evaluate the technology for SONAR application. Open source WAV files will not
be provided by the government during Phase I but will be provided during Phase
The Phase II effort will 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: Define and develop a concept for detecting and classifying acoustic targets
in interference using ML and big data analysis concepts. Demonstrate how the
concept meets the requirements set forth in the Description. Establish
feasibility through analytical modeling and simulation. Provide an estimate of
the amount and type of data required to develop the concept for a sonar
application. 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: Develop and deliver a prototype application that demonstrates accurate
detections (within 30 seconds of initial target energy in water) of threat-like
targets in semi-cluttered environments. Demonstrate, at a Government-provided
facility, the prototype’s capability to meet the performance goals described in
the Phase II SOW. Evaluate the prototype to show it is capable of processing
single WAV files from classified tactical recordings containing interference
sources and threat-like targets of interest as described in the Description.
Classified acoustic recordings with associated truth information will be
provided to the performer for prototype development.
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. It will be tested in an operationally relevant tactical baseline to
determine if the tools meet the requirements of the AN/SQQ-89A(V)15 program in
an integrated tactical system-level environment. Additional experimentation and
refinement will be required during this phase. The prototype will be integrated
into the AN/SQQ-89A(V)15 Program of Record. The product will be validated by
the Test and Evaluation Support Group (TEASG).
This technology may be useful in commercial sonar applications such as marine
mammal detection and tracking and underwater search and rescue applications.
Shamir, L. “Classification of large acoustic datasets using machine learning
and crowdsourcing: application to whale calls.” Journal of the Acoustic Society
of America, February 2014, 135(2): pp. 953-62. Doi 10.1121/1.4861348; https://www.ncbi.nlm.nih.gov/pubmed/25234903
Dia, Wei. “Acoustic Scene Recognition with Deep Learning.” Machine Learning
Department, Carnegie Melon University. https://www.ml.cmu.edu/research/dap-papers/DAP_Dai_Wei.pdf
Halkais, Xanadu C. “Classification of mysticete sounds using machine learning
techniques.” Acoustic Society of America, July 2013. https://www.ncbi.nlm.nih.gov/pubmed/24180760
Dugan, Peter J., Rice, Aaron A., and Urazghildiiev, Ildar R. “North Atlantic
Right Whale acoustic signal processing: Part 1. Comparison of machine learning
recognition algorithms.” 2010 IEEE Long Island Systems, Applications and
Technology Conference (LISAT), 7 May 2010, pp. 1-6. https://ieeexplore.ieee.org/document/5478268/
Detection and Classification of Signals in Noise; Automated Machine Learning;
Big-Data analytics; Deep Learning; Automated Detection and Classification of
Torpedo-like Threats; Digital Signal Processing to Support Correct
** TOPIC NOTICE **
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