TECHNICAL POINT OF CONTACT (TPOC)
Raghu Raj
Clement Kam
PROJECTED CMMC LEVEL REQUIREMENT
Level 1
TECHNOLOGY AREAS
Electronics
|
Information Systems
|
Sensors
MODERNIZATION PRIORITIES
Integrated Network Systems-of-Systems
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Integrated Sensing and Cyber
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Trusted AI and Autonomy
KEYWORDS
Decentralized RF systems, radar, communications, target inference
OBJECTIVE
Develop techniques for decentralized radar systems that will enable cooperative target inferences across tactical networks with N = 3 or more nodes while minimizing/optimizing required per-node effective communications bandwidth. Target inferences include detection and identification of sensed surface targets, and cooperative refers to the radar systems in the decentralized team working cooperatively to improve the confidence of target inferences through information fusion. These proposed techniques must establish the theoretical foundations and communication architectures at the physical and network layers necessary to support high-fidelity imaging and automatic target recognition (ATR) of maritime targets within a low end-to-end latency (which includes processing and network exchanges across the nodes). Proposals should clearly define their level of data fusion (e.g., decentralized signal-level, feature-level, or decision-level fusion) and the anticipated communications bandwidth requirements.
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
Distributed sensing radars serve as an important component of the Joint Force strategy for situational awareness, e.g., US Navy's vision of Distributed Maritime Operations (DMO) [1] and advanced tactical imaging research. By networking physically dispersed radar nodes into a unified cooperative mesh, the Joint Force can fuse information to enhance surface target detection, track and identification. Furthermore, coordinating information from these dispersed nodes overcomes traditional single-platform limitations [1] such as single points of failure and limited angular diversity, especially in contested, high clutter environments. The transition to decentralized sensing and DMO fundamentally shifts the burden of performance from individual platform sensors to the underlying communication networks.
Functioning as a cohesive, decentralized system requires synchronization and low-latency data links. This is especially critical for imaging nodes. The fidelity of a decentralized system is directly proportional to the amount of data the network can move. Streaming raw radio frequency (RF) data allows for maximum imaging resolution but severely strains network capacity. Conversely, enabling nodes to process their own radar data and only sharing images or features reduces bandwidth requirements but results in degraded imaging in collaborative scenarios. Consequently, there is an operational need for new architectures and processing frameworks that explicitly relate communication, computation and sensing resources to surface target detection, tracking and identification performance metrics.
To address these challenges, this SBIR topic seeks solutions that jointly optimize communications and target inference in a decentralized radar sensing framework [2, 3, 4] that processes data locally and only transmits actionable intelligence or compressed imaging features into the network.
PHASE I
Phase 1 consists of the following activities.
Utilize Modeling and Simulation (M&S) and algorithm development to demonstrate the feasibility of the architecture.
Identify critical technical barriers, specifically data link bandwidth limitations.
Establish quantitative performance baselines for both communication network capacity and radar target-inference accuracy.
Evaluate the M&S performance of the developed architecture against the established quantitative baselines.
Deliver a functional Proof-of-Concept (PoC) software or algorithmic implementation demonstrating the decentralized inference approach.
Deliver a comprehensive Technical Feasibility Assessment detailing the M&S results and algorithm performance.
Deliver a Phase II Development Plan that details the transition of the PoC to operationally relevant decentralized radar sensing scenarios (e.g., Hardware-in-the-Loop).
PHASE II
Phase II consists of the following Activities.
Develop, integrate, and validate the Phase I prototype technologies within a representative, customer-informed, decentralized radar sensing framework.
Demonstrate the prototype's performance across three core domains: communications, radar information fusion, and target inference.3. Utilize government provided datasets for all target inference and information fusion validation.4. Validate system performance under representative operational conditions.5. Explicitly quantify improvements achieved by the prototype in both communication efficiency and target recognition performance.6. Demonstrate the scalability of the decentralized techniques across a network consisting of multiple (i.e., >2) sensing nodes.7. Evaluate and document system performance when subjected to communication-constrained networks.
PHASE III DUAL USE APPLICATIONS
Technologies developed under this task will be applicable to a broad range of both DoW and commercial sensing applications. Military applications include decentralized radar systems, cooperative intelligence, surveillance and reconnaissance (ISR), decentralized airborne sensing, and autonomous sensing swarms. Commercial applications include decentralized remote sensing, infrastructure monitoring, environmental monitoring, autonomous transportation systems, smart-city sensing networks, collaborative robotics, industrial inspection and persistent situational systems.
REFERENCES
Congressional Research Service, "Defense Primer: Navy Distributed Maritime Operations (DMO) Concept," 2024. [Online]. Available: https://www.everycrsreport.com/reports/IF12599.html.
H. D. Griffiths and C. J. Baker, An Introduction to Passive Radar, Artech House, 2017.
D. Gunduz and e. al, "Beyond Transmiting Bits: Context, Semantics, and Task-Oriented Communications," IEEE JSAC, 2023.
Maatouk, Ali, Mohamad Assaad, and Anthony Ephremides. "Semantics-empowered communications through the age of incorrect information." ICC 2022-IEEE International Conference on Communications. IEEE, 2022.
https://www.bwcoconsulting.com/fod/osw26bz01-dv001v2
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