Big Data Mining for Maritime Situational Awareness
Navy SBIR 2020.1 - Topic N201-022
NAVAIR - Ms. Donna Attick - email@example.com
Opens: January 14, 2020 - Closes: February 12, 2020 (8:00 PM ET)
AREA(S): Air Platform
PROGRAM: PMA263 Navy and Marine Corp Small Tactical Unmanned Air Systems
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 techniques to mine big data sources for information to use
as reference knowledge by Situational Awareness (SA) applications for improving
Maritime Situational Awareness (MSA).
MSA applications have a need for sustainable sources of reference knowledge. A
robust tactical surface picture requires the mining and effective utilization
of massive amounts of information suitable for ingestion by machine learning (ML)
engines. Gathering the desired understanding of the tactical situation can be
resource intensive and difficult to sustain without the assistance of ML
engines to make sense of it. This SBIR topic seeks to explore the feasibility
of satisfying this need through the exploitation of big data sources. For U.S.
Navy maritime operations, the MINOTAUR Family of Services (MFoS) is the means
by which we plan to correlate and fuse sensor data, producing an integrated
display shared across air, sea and subsurface platforms, and command centers.
The desired result is a coherent battlespace awareness, fusing tactical sensors
with national data to support synchronized actions in the maritime environment.
Optimally leveraging this huge amount of information at the individual platform
level to contribute to this shared tactical picture is extremely challenging
without additional tools to make sense of it all. This is particularly true on
airborne platforms where operators must manage multiple sensor systems
simultaneously. MFoS populates and maintains a Tactical All Source Repository
(TASR) containing vessel tracks derived from multiple cooperative and
non-cooperative sensors, associated radar and optical imagery and electronic
warfare information. In addition, MFoS captures, as available, vessel
classification and identification information made by cooperative broadcast, by
electronic interrogation, by operators or operator aids. While the accumulation
and display of all of this information is quite efficient, quickly understanding
its tactical relevance is challenging particularly with regard to detecting or
predicting threating or anomalous behaviors. Ultimately, the goal is to
understand who is operating in your area of responsibility, what are they
doing, and if they pose a threat, in the most efficient manner possible.
Design and demonstrate the ability to interface with one or more existing
sources of big data and examine/demonstrate the feasibility of the following:
Mature the Phase I-developed algorithms and architectures and apply them for
use with the MFoS TASR. The Government will provide exemplar data files to
support this development. Utilize the style guidelines provided to maintain
uniformity of information presentation with the MFoS operating environment.
Establish a lifecycle maintenance plan. Develop requirements for and conduct
performance assessments of the tool.
DUAL USE APPLICATIONS: Integrate the technology into the Navy’s MFoS
application utilizing its TASR as the source of big data. Big data mining would
benefit a wide range of commercial applications ranging from exploring trends in
social media to analysis of financial systems.
1. Challa, J.
S., Goyal, P., Nikhil, S., Mangla, A., Balasubramaniam, S. S., & Goyal, N.
DD-Rtree: A Dynamic Distributed Data Structure for Efficient Data Distribution
Among Cluster Nodes for Spatial Data Mining Algorithms. 2016 IEEE International
Conference on Big Data (Big Data) (pp. 27-36). Washington DC: IEEE. https://ieeexplore.ieee.org/document/7840586
2. Sun, P.,
Xu, L., & Fan, H. RHAadoop-Based Fuzzy Data Mining: Architecture, Design
and System Implementation. 2016 IEEE International Conference on Big Data
Analysis (ICBDA) (pp. 1-52). Hangzhou, 2016: IEEE. https://ieeexplore.ieee.org/document/7509796
Data; Information Analysis; Analytics; Minotaur; Tactical All Source
Repository; Maritime Situational Awareness