Signal Classification and Anomaly Detection in Contested Spectral Environments

SBIR · Phase: BOTH · Topic OSW26BZ05-DV022 · Solicitation 26.BZ

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 the limited manpower available with this expertise, this capability will dramatically increase the capacity of SIGINT analysis and processing functions which are critical features for C5ISR systems. Standard machine learning approaches are often insufficient as they require massive, labeled datasets that do not exist for future conflicts and frequently produce "black box" solutions that are difficult for commanders to trust, interpret, or certify. This topic seeks an alternative approach for an ML-based solution that can classify RF signals based on similar patterns of emission and provide the flexibility of integration on COTS platforms to facilitate integration with existing C5ISR systems. The desired ML capability will be modular and scalable to fine-tune classification inference results for varying base modulations and frequency ranges. The proposed solution and approach must demonstrate the following critical attributes: 1. Dominant Performance: The system must generate run-time identification and classification inferences that are demonstrably faster as compared to expert SIGINT analysts in complex RF environments scenarios. 2. Human-Interoperable: Generated results must be transparent and understandable, composed of modular components (i.e., not a monolithic neural network). Commanders must be able to understand the "why" behind the recommendations. 3. Scalable: The approach must be capable of scaling from tactical engagements (e.g., individual flight combat) to operational-level scenarios involving thousands of assets across multiple domains (air, sea, land) and extended time horizons. 4. Computational Efficiency: The solution must operate effectively on modest computational footprints (e.g., single or small-cluster CPU-based workstations), avoiding reliance on cost-prohibitive, large-scale GPU clusters for its core training and inference loops. 5. Improved Accuracy: The approach must describe the methodology and metrics for improved accuracy over time as additional data sets, results, and resources are employed. 6. Delivery & Integration: The approach must describe the Continuous Integration / Continuous Deployment methodology to include automation for rapid development changes, testing, and containerized deployment. This solicitation is for a Direct to Phase II (D2P2) award. Offerors are expected to have already achieved significant technical maturity and be prepared to demonstrate existing capabilities upon request.

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).

Document requirements were not captured (needs verification). Check the official announcement.

Application process not captured.

Evaluation process not captured.

Process steps were not captured.

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 — OSD. 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