OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Integrated Sensing and Cyber;Trusted AI and Autonomy 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 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. OBJECTIVE: Develop decision making tools and processing techniques to dramatically reduce the time required to achieve maritime situational awareness in very dense contact environments. DESCRIPTION: Distributed maritime operations in dense surface contact littoral environments is challenging and requires automated mission prosecution. The construction of surface picture heavily relies on the use of a sensor resource manager controlling a surveillance radar augmented by electro-optical/infra-red (EO/IR) and electronic support measures (ESM). The sensor resource manager enhancements would ideally demonstrate improved classification of dark surface contacts not transmitting automatic information systems (AIS). Surface contact physical attributes (e.g., length), behavioral characteristics, and in-theater location should be contributors to the automated decision processes. The aggregation and exploitation of historical information could utilize artificial intelligence/machine learning (AI/ML) methods as appropriate to facilitate in-mission decision processes. In dense maritime environments typical of many areas of the western Pacific, an airborne surveillance platform with a capable radar, ESM and AIS may have several thousand or more vessels under track. Making sense of what is going on is extremely challenging. The automatic association of information from these independent sensors is certainly beneficial in gaining maritime situational awareness. However, in many instances AIS messages contain false coordinates, incorrect field entries or missing entries. In other cases, vessels stop transmitting AIS or AIS reception is jammed. Furthermore, during times of heightened tension or conflict many radio frequency transmissions from surface vessels are expected to curtail dramatically. Achieving a comprehensive wide-area maritime situational awareness in these dense environments is very challenging in the best of circumstances, but is more challenging when the role of AIS and ESM degrades. From a radar perspective, maritime situational awareness involves developing a surface track picture, and then using an inverse synthetic aperture radar (ISAR) mode to image individual vessels in order to classify them. Inverse synthetic-aperture radar (ISAR) dwells may last 15 30 s each, meaning it is impossible to image all vessels under track. In order to address this sensor timeline issue, the Navy needs to gather more classification information from very short duration ISAR sessions. These short sessions or ISAR snap shots (ISARSS) would take approximately 1 s rather than the 15 30 s for a traditional ISAR. The construction of surface picture relies heavily on the use of a sensor resource manager controlling the radar's operation. In this SBIR topic, the Navy seeks to develop the means to maximize the vessel classification information from an ISARSS with sensor resource management control. Minimally, the vessel's length overall and the general topside profile is expected to be derived. This information may be sufficient to identify a vessel as a possible combatant. In order to make ISARSS truly valuable, much more classification information is required. Providing fine naval class-level identification using ISARSS, leveraging compressive sensing techniques would fundamentally change the time and resources needed to achieve wide-area maritime situational awareness. 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 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 NAVAIR 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 an architecture for the automatic aggregation of this information into an exhaustive set of filtering discriminants that can be subsequently used to enhance sensor resource manager decision processes during mission prosecution. Complete an initial analysis of how ISARSS might support fine naval class-level identification with tight coupling between the ISARSS classification information and the sensor resource manager. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Mature the coupled resource management and ISARSS exploitation approach for a specific radar system identified by the Navy sponsor. The ISARSS exploitation approach will be matured using collected field data supplied by the Navy sponsor. Assess the performance of the ISARSS exploitation as a function of range, dwell time and illumination geometry. Assess the performance of the combined system in a high-fidelity mission level simulation. Prepare an integration plan for the integration on a platform identified by the Navy sponsor. Work in Phase II may become classified. Please see note in the Description section. PHASE III DUAL USE APPLICATIONS: Complete the automated control approach and ISARSS exploitation and integrate into a Navy mission system. The automated control and imaging exploitation capabilities could be utilized by agencies like the Coast Guard. REFERENCES: 1. Guerci, J. R. Cognitive radar: A knowledge-aided fully adaptive approach. 2010 IEEE Radar Conference, May 2020, pp. 1365-1370. IEEE. https://ieeexplore.ieee.org/abstract/document/5494403 2. Haykin, S. Cognitive dynamic systems: Radar, control, and radio [point of view]. Proceedings of the IEEE, 100(7), 2012, pp. 2095-2103. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6218166 3. Abad, R. J.; Ierkic, M. H. and Ortiz-Rivera, E. I. Basic understanding of cognitive radar. 2016 IEEE ANDESCON, October 2016, pp. 1-4. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7836270 4. 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 KEYWORDS: Sensor Resource Management; Inverse Synthetic Aperture Radar; Automatic Target Recognition; Maritime Situational Awareness; Radar; Automation
