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Machine Learning Model Development for RF Signal Classification

Active

SBIR / STTR

Response Deadline08/26/2026, 04:00:00 AM UTC

Contract Overview

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

AI Contract Overview

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The Office of the Secretary of Defense is seeking a subcontract for the development of interpretable and modular machine learning models designed to classify RF signals and detect anomalies within contested spectral environments. The primary objective is to ensure operational trust by implementing transparent reasoning within the models to enhance signal classification and anomaly detection capabilities. This opportunity is a Total Small Business Set-Aside under the SBIR/STTR mandate for companies with fewer than 500 employees, falling under NAICS code 511210. The solicitation was posted on August 5, 2026, with a response deadline of August 26, 2026.

General Info

OSD seeks small business subcontracts for interpretable ML RF signal classification and anomaly detection.

Agency

Department of Defense → Office of the Secretary of DefenseView Agency

NAICS

511210 - Software PublishersView NAICS

Place of Performance

Not specified

Set-Aside

SBA

Documents

This scope was carved out of OSW26BZ05-DV022.

The full solicitation package (2 documents), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Signal Classification and Anomaly Detection in Contested Spectral Environments

AI Contract Breakdown

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Timeline

PostedAug 5

subcontract

Response DeadlineAug 26

Submission deadline

Response Deadline

08/26/2026, 04:00:00 AM UTCin 7 days

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Organization & Contact Information

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AgencyDepartment of Defense → Office of the Secretary of Defense

ContactsNo contacts available

OfficeN/A

Organization / Agency

Department of Defense → Office of the Secretary of Defense

View Agency Profile

Office AddressN/A

ContactsNo contact information available

Full Description

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Design and train interpretable, modular machine learning models to classify RF signals and detect anomalies in contested spectral environments, with transparent reasoning for operational trust.

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