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AI/ML Integration for Signal Processing and Threat Classification

Open

Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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This subcontract, issued by the Naval Air Systems Command under the Department of Defense, focuses on the development and integration of AI/ML models to enhance real-time anti-submarine warfare operations. The primary objective is to utilize historical acoustic data within the GRAMS-Cloud environment to enable LOFAR gram generation, signal classification, anomaly detection, and threat identification. The project will be performed in Brunswick, Maine, falling under NAICS code 541715 for research and development in the physical, engineering, and life sciences. This initiative aims to modernize signal processing capabilities to improve the accuracy and speed of identifying underwater threats.

General Info

DoD subcontract for AI/ML development to enhance real-time anti-submarine warfare threat identification.

Agency

Department Of Defense → Naval Air Systems Command

NAICS

541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)

Place of Performance

Brunswick, ME, 04011, USA

Set-Aside

NONE

Documents

This scope was carved out of N00019-25-RFPREQ-APM264-0218.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Upgrades to Global Reconstruction and Acoustic Measurement Suite (GRAMS)

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Timeline

PostedFeb 25

subcontract

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Organization & Contact Information

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AgencyDepartment Of Defense → Naval Air Systems Command

ContactsNo contacts available

OfficeN/A

Organization / AgencyDepartment Of Defense → Naval Air Systems Command

Office AddressN/A

ContactsNo contact information available

Full Description

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Develop and integrate AI/ML models for LOFAR gram generation, signal classification, anomaly detection, and threat identification using historical acoustic data in the GRAMS-Cloud environment to support real-time ASW operations.

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