Level 2 (Self)
TECHNOLOGY AREAS
None
MODERNIZATION PRIORITIES
Human-Machine Interfaces
|
Trusted AI and Autonomy
KEYWORDS
Maritime traffic; Pattern of Life Analysis; PoL; Automatic Identification System; AIS; Automatic Dependent Surveillance Broadcast; ADS-B; System Track Numbers; Anomalous Contacts in congested maritime environments
OBJECTIVE
Develop a capability for automated Pattern of Life (PoL) analysis in congested maritime environments.
ITAR
The 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.
DESCRIPTION
U.S. Navy platforms defended by the Ship Self-Defense System (SSDS) combat system frequently transit maritime regions of the world that are congested with oceangoing vessels and aircraft traffic, which may include fishing vessels, tankers and cargo container ships, commercial airliners, or hostile entities such as enemy surface combatants or anti-air warfare (AAW) threats. In those congested maritime environments, enemies may attempt to hide within the noise of maritime congestion in an effort to gain initiative and surprise against U.S. Navy forces. There are some information sources at the disposal of Ship's Force to detect adversaries. For example, both surface vessels (using Automatic Identification System (AIS) or aircraft (using Automatic Dependent Surveillance Broadcast (ADS-B)) publicly broadcast certain types of information about themselves, including but not limited to Global Positioning System (GPS) location, speed, altitude, destination, and other identifying information as appropriate. However, not all regions have requirements that all traffic must broadcast this information, and actors conducting nefarious or illegal activity have been known to disable AIS and ADS-B systems on their craft. Non-cooperative methods to determine the intent of these craft have been developed in response to the risks posed by uncompliant vessels or aircraft, including PoL analysis. Here, longitudinal records of typical traffic patterns are established over time, then compared against real-time observations to identify anomalous and therefore potentially nefarious or threatening activity that is out of family from those records, such as deviation from established vessel traffic separation schemes (TSS), frequently-traveled flight paths or air corridors, or fishing activity in unexpected areas, among others. Anomalous contacts can then be flagged for increased scrutiny by human operators or other actions. However, detailed monitoring and analysis can be difficult for human operators and watchstanders to do for an entire transit duration or extended stay within a congested environment. For example, it requires close attention to detail over long periods of time, which can induce attentional fatigue and missed indicators by operators. Additionally, in the absence of digital historical records and/or when traversing new areas, Ship's Force may have no historical collective knowledge of maritime traffic patterns against which to compare observations. The safety-critical nature of this task, coupled with the challenge it poses for humans, suggests a unique and important target for automation. Currently no commercial answer to the problem exists.
The Navy seeks the capability to analyze PoL behaviors exhibited by nearby maritime traffic for various regions of the world. Solutions must comprehensively explore all traffic (surface and air) within a 360-degree coverage area around a notional ship, using one or more PoL methods to identify targets that are anomalous and potentially threatening to the ship. Solutions must leverage common sources of maritime traffic data and include at a minimum AIS, ADS-B, and notional air or surface contacts detected by notional radars; other data sources can be specified, but must be realistic for Navy ships to collect, then identify and describe. Tracks or conditions of interest identified by the system must generate alerts for operators via decision support systems or other capabilities that are developed alongside automated analysis and detection logic. Selected alerting content and methods are flexible, but at a minimum must include system track numbers, select descriptive details of the track, provide an explanation of the machine reasoning for the alert that was generated, and compile a machine confidence assessment of the conclusion.
Proposed solutions must (1) function without large volumes of historical traffic patterns and trends stored within the combat system's computers or databases, (2) include a notional plan for future integration with the SSDS combat system and its operator displays, and (3) be accompanied by an architecture that specifies at a minimum: sources of input data required for analysis; communications and/or data exchange pathways to support analysis needs; specific points of integration between algorithm(s) or method(s) used to perform PoL analysis and selected data sources; and operator alerting and information dissemination capabilities that could integrate with SSDS displays.
Proposals should address data volume concerns associated with storing large PoL databases and describe methods to execute the proposal without requiring significant additional data storage devices and without limiting PoL data to temporary region-specific data holdings that must be expunged as ship Operating Areas (OPAREAs) change.
Improved methods of automated PoL analysis that can identify potentially threatening sea or air contacts and communicate findings to watchstanders would significantly improve safety conditions for SSDS vessels transiting these regions.
Three SSDS Top Level Requirements (TLRs) would be supported by this investigation.
The SSDS CS shall generate and display the [Common Tactical Picture] to support command situation awareness and combat coordination. [SSDS_CS_TLR-1222]
The SSDS CS shall provide a means by which operators are notified and are able to participate in the resolution of identification conflicts. [SSDS_CS_TLR-1492]
The SSDS CS shall determine ID and classification with whatever data is available. [SSDS_CS_TLR-1486]
Work should include contacts and composite track data that are produced organically by SSDS combat system sensors, as well as architectural updates that specify the methods or approaches by which PoL analysis will be performed using a fusion of maritime tracks and organic SSDS composite tracks.
Work produced in Phase II may become classified. The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA 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 during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.
PHASE I
Develop a concept for an automated PoL analysis method in congested maritime environments meeting the requirements in the Description. Assess feasibility through modeling, simulation, or other means. Ensure that selected methods are explainable. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.
PHASE II
Develop a prototype automated PoL analysis method in congested maritime environments. Demonstrate functionality and performance of a full 360-degree coverage capable of meeting realistic operational SSDS use cases and needs for specific regions, as well as a detailed plan for integrating this solution with SSDS. ( To support successful Phase II efforts, the Acquisition Office will provide information regarding the SSDS architecture, U.S. Navy consoles and display environment capabilities, and world regions of interest.) Deliver the prototype to the Navy.
It is probable that the work under this effort will be classified under Phase II (see the Description for details).
PHASE III DUAL USE APPLICATIONS
Support the Navy in transitioning the technology to Navy use through system integration and qualification testing for the prototype hardware capability developed in Phase II. Deliver the prototype to support an IWS 80 critical experiment conducted jointly by the performer and the combat system engineering agent (CSEA), to take place in a live environment with tactical SSDS combat system software. ( The transition will require integration of the prototype into SSDS.)
Dual-use applications to consider include but are not limited to sea- or land-based private or third-party transportation, shipping, and logistics applications; personnel security; event security; and environmental and resource extraction regulatory monitoring.
REFERENCES
Bath, W. G. Overview of Platforms and Combat Systems. Johns Hopkins APL Technical Digest, Vol. 35, No. 2, 2020. https://www.jhuapl.edu/Content/techdigest/pdf/V35-N02/35-02-Bath.pdf
Forti, N.
Millefiori, L. M. and Braca, P. Unsupervised extraction of maritime patterns of life from Automatic Identification System data. OCEANS 2019 - Marseille, Marseille, France, 2019, pp. 1-5. DOI: 10.1109/OCEANSE.2019.8867429
Ducruet, C. ed. Maritime networks Spatial structures and time dynamics. Routledge, 2015. https://www.taylorfrancis.com/books/edit/10.4324/9781315692852/maritime-networks-c%C3%A9sar-ducruet
Spiliopoulos, G.
Vodas, M.
Grigoropoulos, G.
Bereta, K, and Zissis, D. Patterns of Life: Global Inventory for maritime mobility patterns. Series ISSN: 2367-2005. EDBT, pp. 770-777, 2024. https://openproceedings.org/2024/conf/edbt/paper-216.pdf
National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. 2004.20 et seq. (1993). https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004
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