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
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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