This solicitation closed on September 23, 2026
View current Department of Defense opportunities →Signal Classification and Anomaly Detection in Contested Spectral Environments
AI Overview
This RFP seeks an interpretable machine learning solution for automated RF signal classification and anomaly detection in contested spectrum environments. The capability must outperform manual SIGINT analysis while remaining computationally efficient, transparent to commanders, and scalable across tactical to operational military scenarios.
This summary is AI-generated from the official solicitation.
Key Details
Official Description
Modern military operations are conducted in contested RF spectrum environments, where adversaries’ actions produce a growing number of complex spectral signatures. The operational need for automation of RF signal classification and anomaly detection using ML techniques addresses threat detection, pattern recognition, and predictive analysis within C5ISR systems – which currently require a manual, human-in-the-loop process. With an exponential increased demand for automated signal processing and ...
Change History
Informational change · Status
Signal Classification and Anomaly Detection in Contested Spectral Environments
Status changed from status: Open to status: Closed.
Informational change · Description
Signal Classification and Anomaly Detection in Contested Spectral Environments
Description information changed.
Informational change · Description
Signal Classification and Anomaly Detection in Contested Spectral Environments
Description information changed.
Signal Classification and Anomaly Detection in Contested Spectral Environments
Summary of Q&A Changes:
The Q&A was reorganized for clarity. Key change: Q1 now directly addresses the period of performance (12 months), moved from Q5/Q6. Duplicate questions Q5-Q6 and Q6-Q7 (both asking about 12 vs. 24 months) were consolidated into single Q1. All substantive technical answers remain unchanged.
Signal Classification and Anomaly Detection in Contested Spectral Environments
Status changed from Pre-Release to Open
Signal Classification and Anomaly Detection in Contested Spectral Environments
Added 4 new Q&As clarifying: RF dataset provision formats and security strategy (Q1), C5ISR system integration approach and Phase I documentation requirements (Q2), ML engine hardening standards (Q3), and GPU/computational footprint flexibility for training vs. inference (Q4). Repeated period of performance clarification (12 months).
Signal Classification and Anomaly Detection in Contested Spectral Environments
This clarification resolves a conflicting statement in the solicitation document by confirming that applicants must plan for a 12-month period of performance for Direct to Phase II proposals, not 24 months, affecting budget allocation and milestone planning.
Signal Classification and Anomaly Detection in Contested Spectral Environments
New opportunity: Signal Classification and Anomaly Detection in Contested Spectral Environments
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