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Estimation of automatic target recognition performance for synthetic aperture sonar with integration angle reduction
Author(s): Julia Gazagnaire; Benjamin McLaughlin
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Paper Abstract

The attraction of synthetic aperture sonar (SAS) is the promise of achieving high resolution across the entire sonar image. However, this theoretical resolution depends on maintaining phase coherence over the full synthetic aperture length or integration angle. There may be some advantages to reducing the integration angle for SAS processing. For example, when considering the large amount of sonar data to be processed for a single mission, there may be a computational savings to be gained by truncating the synthetic aperture length. This could be critical when the system needs to meet post processing time requirements or when sonar data is being processed in situ to enable image queued autonomous behaviors. Another advantage may be to have the option to narrow the integration angle in the case of uncompensated vehicle motion or incorrect estimates of the sound speed. The narrowing may improve the image quality without significantly compromising the information content of the data and the subsequent Automated Target Recognition (ATR) performance. The ATR performance is investigated using simulated SAS data over 10 different integration angles and three backgrounds whose sound speed ratios range from 0.98 to 1.28. An Ada-Boosted Decision Tree classifier was used to calculate the probability of classification (Pc) and false alarm rate (FAR) and generate receiver operator characteristic (ROC) curves. Additionally, a measure of the Fisher information of the contact image snippets is investigated as a function of integration angle and object pose angle.

Paper Details

Date Published: 30 April 2018
PDF: 12 pages
Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 106281A (30 April 2018); doi: 10.1117/12.2306271
Show Author Affiliations
Julia Gazagnaire, Naval Surface Warfare Ctr. Panama City Div. (United States)
Benjamin McLaughlin, Naval Surface Warfare Ctr. Panama City Div. (United States)

Published in SPIE Proceedings Vol. 10628:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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