This solicitation closed on September 23, 2026
View current Department of Defense opportunities →Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
AI Overview
This RFP seeks FPGA-based signal processing solutions using cognitive techniques like deep learning to detect and classify low-probability-of-intercept radar waveforms in real-time. The capability will enhance Navy threat simulation systems' jamming countermeasures by rapidly identifying adversarial radar types.
This summary is AI-generated from the official solicitation.
Key Details
Official Description
DRFMs are typically used to characterize and jam adversarial radar signals. Detecting and classifying LPI waveforms with low latency from adversarial sources is critical to DRFM effectiveness, enabling optimization of jamming countermeasures based on the identified radar type.
Detecting and classifying LPI radar signals is non-trivial and requires significant computing power. Because LPI signals are typically low power, high duty cycle, and wideband, they require long integration times and high...
Change History
Informational change · Status
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Status changed from status: Open to status: Closed.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Added Q9 clarifying RFSoC implementation flexibility: heterogeneous partitioning among programmable logic, embedded processing, and accelerators is acceptable. Phase I must demonstrate latency in hundreds of nanoseconds, design must fit SoC within maximum current limits.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Q&A Changes Summary
New Answer Added to Q1: Clarified that the STTR scope focuses on algorithm development with baseband data access on the FPGA; no specific frequency bands, sampling rates, or SNR floor specified.
New Question Added (Q9): Asks whether cognitive-processing functions must execute entirely in programmable logic or may partition across programmable logic, embedded processing system, and accelerators; requests Phase I latency, resource-utilization, and power characterization details. (Answer pending)
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Key Changes to Q&A:
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New Q1 added with 4 sub-questions covering critical technical specifications:
- Specific frequency bands of interest
- Frequency band coverage of government-provided LPI waveform data
- Required sampling rates for detection model
- Minimum SNR threshold for detection model
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New Q9 added (previously "Q8: Hello!") regarding RFSoC FPGA architecture flexibility:
- Clarifies whether cognitive processing must execute entirely in programmable logic or can partition across programmable logic, embedded processing system, and accelerators
- Requests Phase I characterization metrics (latency, resource utilization, power)
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Question renumbering: Original Q1-Q8 shifted to Q2-Q9 due to new Q1 insertion.
Impact: Proposers now have critical guidance on technical specifications (frequency bands, sampling rates, SNR), data availability, and architectural flexibility for RFSoC implementation—all essential for Phase I proposal development.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
5 new questions added: Q1 asks about LPI waveform data availability during proposal prep; Q2-Q5 address data access, classification levels, CMMC/clearance requirements, and whether ATSO-provided vs. synthetic data should be used in Phase I planning. Q3 clarifies if "research institution point of contact" refers to a pre-identified partner or generic language for offeror selection.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Status changed from Pre-Release to Open
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Added 1 new Q&A with 3 sub-questions regarding GQM-163A platform preference, existing ATSO podded DRFM system integration, and Phase I/II platform/documentation provision. Renumbered subsequent questions; no answer changes to existing Q&As.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Q1 received an answer: DON will select 2-4 proposals for Phase I conventional topic awards. Q2 remains unanswered (Response Pending on RFSoC architecture partitioning flexibility and Phase I characterization requirements).
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
Added 1 new Q&A: Q1 asks whether multiple Phase I contracts will be awarded. Original technical question about RFSoC FPGA architecture partitioning and characterization requirements renumbered as Q2.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
This Q&A clarifies that cognitive processing functions can be distributed across multiple RFSoC components (programmable logic, embedded processor, accelerators) rather than confined to programmable logic alone, but Phase I must demonstrate real-time detection/classification with documented latency, resource utilization, and power metrics.
Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
New opportunity: Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques
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