SBIR/STTR Research Award

Effective Compaction of UAV LiDAR and Other Data Transmission to Maximize Bandwidth and Security

Award identifier 5a6091b1fb1ab5fa84073e2508ec86cd10a0ab4020be4fc5ccca196c3626b8baAwarding agency Department of Defense · Air Force

Award amount

$74,801

Program and phase

STTR · Phase I

Award year

2023

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Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
ATOMBEAM TECHNOLOGIES INC.
Proposal award date
2022-11-02
Contract end date
2023-02-06
UEI
WCN5DNZBCZV8
Solicitation number
X22.D
Topic code
X22D-OTCSO1

Research abstract

AtomBeam proposes to incorporate a significant modification to its Compaction product, called count sketch, to enable Compaction to encode LiDAR data from UAS as well as other data streams. Compaction in its current release is capable of encoding most typical UAS data, other than video. AtomBeam’s process entails building Codebooks from representative sample datasets using machine learning. Codebooks are loaded on a source and a destination, and with the Compaction runtime executable, they act as translation dictionaries, allowing greatly reduced datastreams to be transmitted losslessly (fully reversibly) in two-way communication. Typical runtime reduction of data size is 75% for machine/IoT data. The result is an average 4x expansion of effective bandwidth for most users. For most data types, samples of 1 – 10 MB of representative data are all that is required to build a Codebook. In an average of 10 minutes in processing time, Compaction builds 50 Codebooks using machine learning, seeking the most efficient data pattern length to achieve maximum Compaction in runtime. LiDAR, however, and some RADAR data are very high volume and sample datasets can be multiple gigabytes in size, which is impractical for Codebook generation without modification of the Compaction code. Count sketch is a sophisticated mathematical approach for dimensionality reduction that can make Codebook generation from very large datasets practical. With this added capability, Compaction should be capable of building Codebooks from any LiDAR or RADAR dataset, irrespective of its size. This would enable the full suite of the UAS datastream other than video (which eliminates patterns by using “lossy” compression) to be compacted, and therefore effectively realize a 4x improvement in effective bandwidth. This Phase 1 STTR will utilize the combined expertise of AtomBeam’s mathematical and scientific personnel with the expertise of members of UMKC’s faculty, who are experts in LiDAR and UAS data communications.

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

7 SBIR/STTR records share this UEI (2018–2025). Reported award amounts total $3,586,305. Reported amounts are not federal obligations.

Phases: Phase I, Phase II – Direct to Phase II, Phase II

Agencies: Department of Defense

Agency intelligence

Department of Defense

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