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...
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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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