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Perceptually Lossless Unmanned Underwater Vehicle (UUV) Sensor Data Compression

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0640
Agency Tracking Number: N231-038-0305
Amount: $139,991.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N231-038
Solicitation Number: 23.1
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-08-04
Award End Date (Contract End Date): 2024-02-05
Small Business Information
153 Langtree Campus Drive
Mooresville, NC 28117-1111
United States
DUNS: 040707460
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Xianyang Zhu
 (704) 799-6944
 xianyang.zhu@corvidtec.com
Business Contact
 Tracy Williams
Phone: (405) 410-6552
Email: tracy.williams@corvidtec.com
Research Institution
N/A
Abstract

Data through water transfer rates are generally low due to limited bandwidth. The sonar images must be efficiently compressed to increase onboard storage and enable large amounts of data through water transfer. Mathematically, the sonar images can be viewed as a linear combination of some appropriate Green’s functions. Thus, they are highly coherent correlated and the associated matrices are rank deficient. This unique property can be exploited to achieve better sonar image compression. To this end, Corvid Technologies (Corvid) and Lawrence Berkeley National Laboratory (LBNL) propose to develop an efficient data-driven dictionary sparse coding-based sonar image compression algorithm. Firstly, the team will employ butterfly factorization to extract the salient features at different levels from the training data, then we will use a deep dictionary learning algorithm to build a more accurate dictionary by taking the butterfly factorization results as input. Once the dictionary is learned, Corvid and LBNL will use a Bayesian compressive sensing approach to extract the sparse coefficients of each image more efficiently. For the scenarios that multiple sonar images are associated with the same scene, we will use a multi-task Bayesian compressive sensing method to obtain more noise robust results by exploiting the joint information among those images. Due to sparse coding, the extracted coefficients can serve as features for better automatic target recognition performance.

* Information listed above is at the time of submission. *

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