Detection and Classification of Low Probability of Intercept radar waveforms using Field-Programmable Gate Array with Cognitive Techniques

STTR · Phase: BOTH · Topic DON26TZ05-NV023 · Solicitation 26.TZ

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 receiver sensitivity. While these Radio Frequency (RF) considerations are fundamental challenges in LPI detection, this effort will focus on signal processing techniques for LPI detection and implementation on a FPGA, with emphasis on detection methods. Traditional signal processing methods include, but are not limited to, adaptive matched filtering with Fast Fourier Transform (FFT) frequency detection, various filter banks, autocorrelation, Wigner-Ville distribution, and cyclostationary processing. Leveraging FPGA technology may provide performance gains through more advanced cognitive techniques such as time-frequency analysis, convolutional neural networks (CNNs), and deep learning methods including long short-term memory (LSTM) networks. In fact, the FPGA is the ideal tool for this, since its hardware emulation of algorithms allows for real-time or near real-time performance. DRFM RFSoCs targeted for Test and Evaluation (T&E) threat-representative flight systems can be retrofitted with this capability, as FPGAs can adapt hardware description language (HDL) from other implementations. Implementing this system on a DRFM platform presents challenges due to HDL requirements and the relatively recent adoption of direct-RF architectures to access wide frequency ranges. The Airborne Threat Simulation Organization (ATSO) at the Naval Air Warfare Center, Weapons Division (NAWCWD), Point Mugu, California, is responsible for simulating real adversarial threats for Navy training. ATSO has access to extensive LPI waveform data that can support training, testing, and validation of deep learning algorithms, convolutional neural networks, or other knowledge-based techniques. Leveraging this a priori knowledge, the research institute point of contact, will identify, optimize, and develop cognitive algorithms suited to ATSO applications. If multiple algorithms are identified, they may be implemented in parallel. Because real-time hyperparameter adjustment on the FPGA is not practical, neural network or cognitive techniques must be designed and trained on a high-compute PC or Linux platform, if required. Other algorithms not dependent on deep learning or cognitive techniques can be developed in hardware description language (HDL) and behaviorally simulated using software such as ModelSim. Benchmarking development metrics should be straightforward, as ATSO can provide ground truth data. An evaluation kit of the Advanced Micro Devices (AMD) (formerly Xilinx) RFSoC is proposed to prototype this capability. The final deliverable will be a report assessing the feasibility and effectiveness of implementing advanced cognitive techniques on RFSoCs.

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

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Application process not captured.

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Generated by local:rules-v1 · 3h ago

The announcement does not say what has to be submitted, so the disclosure exposure cannot be judged. the scope of the material requested is not stated. The awarding body is U.S. Department of War — NAVY. 8 fields could not be read and must be checked in the announcement and the grant agreement.

This assessment is derived from the stored announcement, document and contract fields. Anything the source does not state is marked "needs verification" — it never asserts that your idea will be taken. Generated by local:rules-v1 · 3h ago

Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.08.20

Human review

Not reviewed — check the official announcement

First seen

2026.08.20

Data status

Auto-collected, not reviewed

AI enrichment

local:rules-v1 · 3h ago