DON26BZ03-NV065 TITLE: Adaptive Sensor Management

OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)

COMPONENT TECHNOLOGY PRIORITY AREA(S): Advanced Computing and Software; Integrated Sensing and Cyber

PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)

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 an algorithmic capability for dynamic resource allocation that characterizes existing Ship Self-Defense System (SSDS) sensor tasking allocations, the relative magnitude of each sensor’s fire control data contributions to composite tracks, identify sensor resources that could be released for other more impactful tasking without sacrificing current track quality metrics of relevance, and specify existing or potentially new sensor tasking that would benefit most from re-allocation of those resources.

DESCRIPTION: Navy aircraft carriers and amphibious warfare (L-class) ships are defended by the SSDS, a combat system comprised of weapons, sensors, communications systems, computers, and other elements working together to detect, track, and engage inbound anti-ship missiles and other threats. SSDS platforms sense their environments and identify tracks of interest by integrating inputs from a variety of sensors, which include rotating, fixed face and fire control or target illumination radars that cover a variety of radar bands, as well as electronic support (ES) sensors that process received Radio Frequency (RF) waveforms. Each of these sensors provides its update to the combat system at different rates. For example, while phased array radars can provide rapid target measurements and schedule beams or dwells across a wide field of view, rotating radars may have much narrower fields of view (FOVs) and provide full rotations only once every several seconds. However, because each sensor strives to maximize performance and provide the information necessary for SSDS to build and maintain fire control quality tracks on targets of interest, there are conditions in which further aggregation of sensor data may provide diminishing returns related to fire control track quality (e.g., continuing to provide updates on certain well-characterized tracks may not offer significant track state covariance reductions or additional fire control quality improvements over its current state). It may be advantageous in these cases to shift some of those sensor tasks to other combat system needs, specifically where those additional tasks could substantially improve track quality on other targets or help improve situation awareness via other means. Nothing available commercially can provide this capability.

The Navy seeks an algorithm-based software solution that automatically detects which sensors are contributing to fire control quality tracks on particular targets, assesses the relative magnitudes of their contributions, identifies conditions in which particular sensor resources could be released for other sensor tasking, and specifies which current or potentially novel sensor tasking would benefit most from allocation of those released resources. Proposed solutions should be dynamic, adaptive, responsive to rapid changes in track hostility characterization (i.e., solvable in real-time or better, minimizing algorithmic worst-case time complexity), and work with heterogeneous combinations of sensor tasking and resource utilization feedback parameters. Solutions must identify each sensor’s capability that is controllable by SSDS (e.g., search sectors, search modes, track-based controls, and cueing capabilities, among others) and leverage those realistic features in a solution for SSDS.

Examples of alternative sensor tasking include but are not limited to: executing surface-, volume-, or sector-specific search patterns; modifying or updating search modes; applying track-based controls; cueing other sensors on a specific target; or other actions. Example algorithmic techniques and fields from which approaches could be derived include stochastic and Bayesian optimization, metaheuristics, model predictive control theory, or others. Proposals using artificial intelligence and machine learning approaches will also be considered, but proposers should note that candidate solutions must be capable of generating resource re-allocation recommendations in scenarios that may be completely novel to the combat system and for which little to no prior exposure has been provided. Finally, proposed solutions should correspond to and be compatible with the existing SSDS Program of Record sensors. The initial solution will focus on mathematical and algorithmic development needed to address interactions between four radars that either are or will be installed on most SSDS platforms: SPS-48, SPQ-9B, MK-9 Tracker/Illuminator, and SPY-6(V)3. Solutions should be demonstrable under low to medium-fidelity modeling and simulation approaches, and the algorithmic solutions included in the proposed solution must be explainable.

Five SSDS Top Level Requirements (TLRs) would be supported by this investigation (note that, in the requirements language below, EW signifies Electronic Warfare, and ES signifies Electronic Support):

• The SSDS Combat System (CS) shall provide a sensor cueing capability that automatically selects and assigns air tracks to specified own ship sensors for the purpose of achieving requisite track confidence and track data quality to support automatic engagement recommendations at maximum range allowed by engagement doctrine. [SSDS_CS_TLR-289]

• The SSDS CS shall perform cued radar search for high-priority ES tracks that meet specified criteria but are not correlated or associated with existing SSDS CS active radar tracks. [SSDS_CS_TLR-291]

• The SSDS CS shall detect resource utilization conflicts between sensors and resolve them based on the sensor resource priorities established. [SSDS_CS_TLR-1300]

• The SSDS CS shall have automated and manual capabilities to request additional target EW data by ES sensor(s) for a specified track to support updates to EW classification. [SSDS_CS_TLR-1607]

• The SSDS CS shall coordinate above water radar activities based on radar capabilities, availability, and tactical and operational conditions. [SSDS_CS_TLR-1631]

Solutions explored during a potential Phase II award must include an expanded set of sensors, the last of which is an ES sensor. The full sensor suite will therefore include SPS-48, SPS-49, SPQ-9B, SPY-6(V)2, SPY-6(V)3, MK-9 Tracker/Illuminator, and SLQ-32(V)6.

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 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 a dynamic resource allocation software capability that characterizes existing SSDS sensor task allocations and provides a solution that meets all requirements identified in the Description. Show feasibility of the concept using modeling, simulation, analysis, or other methods that are explainable, as well as references from sensor tracking and resource management open literature for resource management inputs. (Note: To support realistic demonstration and candidate solution development, the performer will be provided with a reference combat system architecture example and additional sensor tasking information.) Phase I solutions will be advisory in nature, where recommendations will be provided to sensor and/or combat system operators for evaluation and action. If the Phase I Option is exercised, include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop a prototype dynamic resource allocation algorithm-based software capability that characterizes existing SSDS sensor tasking allocations based on the results of Phase I, expanding to include the Phase II sensors identified in the Description as well as SSDS-specific fire control quality tracking and sensor resource management details. Phase II will also include a trade study to explore overall system performance where resource allocation actions are automatically taken by the system vice made to human operators for consideration and possible action. (Note: Phase II will require a notional plan for integrating the product into the SSDS combat system.) 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 proposer and the combat system engineering agent (CSEA), expected to take place in a live environment with tactical SSDS combat management system (CMS) software. (Note: The transition will require integration of the prototype into the SSDS CMS.)

Dual-use applications to consider are self-driving cars, vehicles, and other platforms equipped with multiple sensors; manufacturing and production quality control systems; and other applications where systems must dynamically prioritize and allocate sensor coverage to maintain maximum system efficiency.

REFERENCES:

1. 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
2. Li. Zhize et al., "Heterogeneous Sensing for Target Tracking: Architecture, Techniques, Applications and Challenges." Measurement Science and Technology, Vol. 34, No. 7, pp. 1-25. https://iopscience.iop.org/article/10.1088/1361-6501/acc267/pdf
3. Li, Tianqi; Krakow, L.W. and Gopalswamy, S. "Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies." 2021 IEEE Conference on Control Technology and Applications (CCTA), San Diego, CA, USA, 2021, pp. 131-137. doi: 10.1109/CCTA48906.2021.9658603. https://arxiv.org/pdf/2102.02919; https://ieeexplore.ieee.org/document/9658603
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: Target Illumination Radars; Sensor Resources; Dynamic Resource Allocation; Bayesian Optimization; Model Predictive Control Theory; Alternative Sensor Tasking

TPOC 1: Tilksew Ashagre
(202) 781-2990
tilksew.a.ashagre.civ@us.navy.mil

TPOC 2: Christin Staples
(202) 781-4277
christin.n.staples.civ@us.navy.mil