SBIR/STTR Research Award

Precision Enhancement of Airdrop Releases through Learning (PEARL)

Award identifier 34dadee87f7910fc2aee08da41fa2305fd046929427aa55ba00d9ea61ca669d7Awarding agency Department of Defense · Air Force

Award amount

$149,916

Program and phase

SBIR · Phase I

Award year

2015

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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

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

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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