Semantically-Aware ISR

SBIR · Phase: BOTH · Topic DPA26BZ05-DV019 · Solicitation 26.BZ

Effective Intelligence, Surveillance, and Reconnaissance (ISR) in contested theaters depends on the ability to extract and transmit decision-relevant information from sensors under severely constrained communications. Modern multimodal sensor payloads, including electro-optical (EO), infrared (IR), event-camera, low-light, and platform-telemetry streams, produce data volumes orders of magnitude beyond what tactical communications links can carry, particularly when those links are degraded, intermittent, jammed, emission-constrained, or forced to single-digit kilobits per second by electronic warfare. The operational consequence is binary: an ISR platform either delivers mission-relevant evidence to the operator under these conditions, or it provides limited operational value. Small unmanned aircraft systems (sUAS) embody this challenge in its most acute form. Representative Group 1 platforms such as RQ-11B Raven and RQ-20 Puma-class systems carry EO and IR video for dismounted operators with single-digit-watt payload compute budgets. Representative Group 2 platforms such as ScanEagle-class systems provide broader reconnaissance with greater range, endurance, and modular sensors, but remain size, weight, and power (SWaP) constrained. Across both classes, full-motion EO and IR video routinely exceeds available tactical link capacity, particularly when multiple unmanned systems operate simultaneously. Three concurrent technical failure modes define this problem space and rule out incremental approaches: • Mission intent is vague and operator-defined. Operators do not request a single class label. They request answers to compositional, context-dependent tasks such as identifying indicators of hostile staging along a ridgeline, judging whether a gathering is consistent with prior pattern-of-life, or noticing whether access patterns to a structure have changed in the last twenty minutes. There is no fixed object dictionary, and what is mission-relevant in one engagement is clutter in the next. • Regions of interest are not necessarily objects. Mission-relevant regions are frequently not detectable as discrete objects in any single frame. Relevant evidence may be a vehicle stopped where vehicles do not normally stop, a crowd dispersing unusually fast, a heat signature appearing at an unusual time, a person loitering near an asset, or a pattern of vegetation disturbance accumulated over many frames. Object detectors and large vision-language models are weak in this regime because the evidence is distributed across space and time rather than localized in a single frame. • The platform cannot host heavy artificial intelligence. Group 1 payload compute is typically constrained to single-digit watts; Group 2 platforms allow modest growth but remain size, weight, and power constrained. Large vision-language models and conventional deep-learning trackers are not deployable at this power envelope and are additionally fragile under domain shift, adversarial obscuration, and data-poor conditions characteristic of low-density and low-prior threats. This topic seeks a new class of mission-aware semantic communication systems that can approach two orders of magnitude data reduction in mission-suitable ISR scenarios while preserving mission-relevant context and semantic content. The required capability is not video compression, object detection, or target tracking; it is an onboard, real-time, low-power information-processing layer that understands operator intent, reasons over spatial and temporal context, captures behavioral evidence distributed across frames, and emits only the compact semantic packets needed for receiver-side reconstruction. Semantic packets shall combine high-fidelity regions of interest, tracklets, scene-change descriptors, temporal evidence, low-rate background context, confidence, uncertainty, and traceable explanations sufficient for the operator to act on the evidence at substantially lower bandwidth. Phase II prototype…

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 — DARPA. 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