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Award details
- Awarding agency
- Department of Defense
- Branch
- Air Force
- Recipient company
- AURORA FLIGHT SCIENCES CORPORATION
- Proposal award date
- 2015-07-13
- Contract end date
- 2016-03-30
- UEI
- K1L1BZLNPWC4
- Solicitation number
- 2015.1
- Topic code
- AF151-058
Research abstract
ABSTRACT:Airdrop accuracy is of paramount importance since airdrop systems landing in unintended locations could create internationally damaging outcomes. There is an opportunity to compensate for local effects by learning their impact on the airdrop trajectory from previous missions. Aurora Flight Sciences is teaming with Boston University to apply deep learning techniques to historical airdrop data at particular drop zones to determine site- and mission-specific biases and increase accuracy at that drop zone. Deep learning can identify complex multi-layer relationships within data and discover patterns without any prior knowledge, enabling detection of trends that are only present when multiple conditions are present. Although deep learning calculations are computationally expensive, they can be run offline to generate a database that can be accessed online based on real-time mission information. The algorithms can ingest additional data as it is collected to update and refine outputs for even greater accuracy. The conceptual tool developed in this Phase I SBIR will plan flight paths and computed air release points based on knowledge learned from past airdrops with the ultimate goal of showing the feasibility of using historical data to improve accuracy at a particular drop zone.BENEFIT:Recent operations in Afghanistan and Syria have increased the interest in precision airdrop as a low cost and safe method of tactical resupply, and mission commanders are continuing to seek ways to improve airdrop accuracy and reliability. Calculation of site- and mission-specific airdrop adjustments will break through the current barrier in airdrop accuracy created by limitations in weather forecasting. Increases in airdrop accuracy reduce risk to our ground and air forces, increase bundle recovery rates, and enable missions at more highly constrained drop zones. Further, this deep learning based technique can be extended to other airdrop system types including humanitarian, dispersion, and guided systems. The tool developed in this effort can be integrated into multiple airdrop mission planning programs including the Consolidated Airdrop Tool, XDrop, and PARANAVSYS. Additionally, it can be extended to high altitude sonobuoy deployment for the Navy.
Technology keywords
Not reported
Other awards by this recipient
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Awards with the same topic code
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Research funding history
172 SBIR/STTR records share this UEI (1990–2017). Reported award amounts total $52,953,955. Reported amounts are not federal obligations.
Phases: Phase I, Phase II
Agencies: Department of Defense, National Aeronautics and Space Administration, National Science Foundation, Department of Commerce
Contractor profile
AURORA FLIGHT SCIENCES CORPORATION
Historical federal award activity, when published, is on that contractor profile. Matched by UEI only.
Agency intelligence
Department of Defense
NAICS intelligence
This classification comes from the contractor profile, not from the SBIR award record.
Contractor industry classification NAICS 541512
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