Dynamic Sensor-Tasking and Routing System for Subsurface Hazard Detection
U.S. Department of Defense – DTRA (SBIR)
- Funder
- U.S. Department of Defense – DTRA (SBIR)
- Award amount
- Not specified
- Deadline
- January 6, 2027
- Eligible countries
- United States
Summary
SBIR topic DTR27BZ02-NV003 (U.S. Department of Defense – DTRA), Phase I and II. Open to U.S. small businesses. Opens: December 2, 2026. Closes: January 6, 2027. Current field-deployable capabilities for WMD device and component defeat, route reconnaissance, and sensitive site exploitation have made significant strides in rapid surface-level mapping and visual threat triage using unmanned aerial and ground platforms. However, these operational systems encounter a critical bottleneck when confronting subsurface hazards. Visual, optical, and topographic sensors are fundamentally blind to threats that are partially buried, completely subsurface, or obscured beneath soil, blast debris, or disturbed earth. When subsurface geophysical instruments are integrated onto these unmanned platforms to address this blind spot, current field architecture suffers from a severe "collect everything" bottleneck. Lacking localized intelligence, platforms typically execute static, pre-programmed survey scripts which force sensors to operate continuously and indiscriminately across an entire area. This floods constrained tactical communication networks with massive volumes of raw sensor data, overwhelms field computing devices, drastically slows operational tempo, and results in sensor operational blindness when local soil physics degrade or invalidate specific sensor modalities. When conducting standoff reconnaissance of suspected hazard areas—which may contain buried chemical or biological munitions, radiological dispersal device (RDD) components, bulk precursor storage vessels, or associated explosive triggers and conventional ordnance—these bottlenecks force operators into dangerous, prolonged exposure on target. Furthermore, relying on rigid, static classical logic to manage these complex sensor suites in real time introduces a critical limitation, as traditional rule-based programs cannot adapt to highly dynamic, uncharacterized environments where the boundary conditions are not…
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