An in-air synthetic aperture sonar dataset of target scattering in environments of varying complexity

Thomas E. Blanford, David Patrick Williams, J. Daniel Park, Brian T. Reinhardt, Kyle S. Dalton, Shawn F. Johnson, Daniel Brown

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number1196
JournalScientific Data
Volume11
Issue number1
DOIs
StatePublished - Dec 2024

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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