An in-air synthetic aperture sonar dataset of target scattering in environments of varying complexity
Year: 2024
Type: data-paper
Abstract: This paper describes a synthetic aperture sonar (SAS) dataset collected in-air consisting of four types of targets in four environments of different complexity. The in-air laboratory based experiments produced data with a level of fidelity and ground truth accuracy that is not easily attainable in data collected underwater. The range of complexity, high level of data fidelity, and accurate ground truth provides a rich dataset with acoustic features on multiple scales. It can be used to develop new signal-processing and image reconstruction algorithms, as well as machine learning models for object detection and classification. It may also find application in model verification and validation for acoustic simulators. The dataset consists of raw acoustic time series returns, associated environmental conditions, hardware configuration, array motion, as well as the reconstructed imagery.
Source: Scientific Data
Institutions: University of New Hampshire, Pennsylvania State University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,290
Awards: ALTERNATIVE REPRESENTATION OF INFORMATION FOR ACOUSTICS, G2040882162, QUANTIFYING AND ASSESSING MODEL FIDELITY FOR ATR DATA AUGMENTATION, G318340299, G3431549496, G4695206912, G5166336624, HIGH FREQUENCY SYNTHETIC APERTURE SONAR ACOUSTIC TIME SERIES MODEL VALIDATION, INFORMATION GEOMETRY AND INFORMATION STRUCTURE, UNDERSTANDING MODEL FIDELITY FOR ATR DATA AUGMENTATION
OpenAlex ID: W4404070273