N102-145 TITLE: Enabling netted sensor fusion for anti-submarine warfare in uncertain and variable environments

TECHNOLOGY AREAS: Information Systems, Sensors, Battlespace

ACQUISITION PROGRAM: AN/UYQ-100 Undersea Warfare Decision Support System (USW-DSS) ACAT IV-T

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted." The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Develop and demonstrate convergent, dynamic registration of uncertain, biased ASW sensor data from multiple carrier strike group platforms for command and control (C2) to enable shared, distributed classification, localization and prosecution of adversaries in real time.

DESCRIPTION: Net-centric warfare exploits the fact that platforms and sensors can be netted together with communication links to form a system or net that is greater than the sum of its parts.

Sensor netting is fundamentally different from the current practice of transmitting highly processed track information between units. A good example is the Navy's Cooperative Engagement Capability (CEC) which links together air and missile defense assets in ways that allow one platform to guide a missile launched by another at a target first detected by a third. (Ref: Cote and Sapolsky, MIT Security Studies Program) The CEC construct is not directly applicable in the uniquely complex undersea environment; detection ranges against a very quiet target are short, wideband communications all but non-existent and spatial and temporal variability make netted sensor registration requirements difficult to achieve. As a starting point, this solicitation seeks a probabilistic treatment for resolving bias in undersea netted sensor data. The approach must reduce the correlation errors and mis-associations that degrade netted system performance due to bias and correlation inconsistency. The final research product will be a sensor gridlocking algorithm that converges to an accurate registration of uncertain undersea sensor data from distributed ASW sensors on multiple platforms.

PHASE I: Conduct the needed R&D to identify and define the algorithms and processes to enable effective gridlocking and registration of ASW sensors on multiple platforms. Provide the analysis for a candidate approach, document the conceptual designs and provide in a final Phase I report.

PHASE II: Complete the additional R&D required to develop and demonstrate a prototype tool that will incorporate viable candidates to support Gridlocking and Registration of ASW sensors. Provide the assessment of the total impact for the Navy�s improvement in ASW command and control (C2) to improve classification and/or localization of adversaries in time for effective Anti-Submarine actions to be achieved. Deliver algorithm description, procedures for use, test results, Phase III plan.

PHASE III: This algorithm must transition to multiple platforms through multiple programs as an enabler for net-centric ASW. This will be achieved by teaming the SBIR contractor and Navy personnel to accomplish the transition of the technology to the USW-DSS program of record and the Surface Ship Advanced Capability Build program as part of the coordinated fusion and sensor netting programs.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Algorithms/tools can be used in commercial rescue/salvage, and or oil exploration to provide operators with improved correlation among undersea exploration sensors.

REFERENCES:
1. Endsley, M. R., Bolte, B., & Jones, D. G. Designing for Situation Awareness: An approach to human-centered design. London: Taylor & Francis (2003)

2. Hall, David L, McMullen, Sonya A.H., Mathematical Techniques in Multisensor Data Fusion, 2nd ed. Norwood, MA: Artech House (2004)

3. Hall, David L, Llinas, James, Handbook of Multisensor Data Fusion. New York: CRC Press (2001)

4. Blackman, Samuel, Popoli, Robert, Design and Analysis of Modern Tracking Systems. Norwood, MA: Artech House (1999)

KEYWORDS: Network; Fusion; Command and Control; Cognition; Registration

** TOPIC AUTHOR (TPOC) **

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