A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar


Creating human-informative signal processing systems for the underwater acoustic environment that do not generate operator cognitive saturation and overload is a major challenge. To alleviate cognitive operator overload, we present a visual analytics methodology in which multiple beam-formed sonar returns are mapped to an optimized 2-D visual representation, which preserves the relevant data structure. This representation alerts the operator as to which beams are likely to contain anomalous information by modeling a latent distribution of information for each beam. Sonar operators therefore focus their attention only on the surprising events. In addition to the principled visualization of high-dimensional uncertain data, the system quantifies anomalous information using a Fisher Information measure. Central to this process is the novel use of both signal and noise observation modeling to characterize the sensor information. A demonstration of detecting exceptionally low signal-to-noise ratio targets embedded in real-world 33-beam passive sonar data is presented.

Publication DOI: https://doi.org/10.1109/JOE.2017.2784199
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Anomaly,fisher Information,neuroScale,SONAR,visualization
Publication ISSN: 1558-1691
Full Text Link:
Related URLs: https://www.sco ... 76d72aea9921c33 (Scopus URL)
http://ieeexplo ... cument/8252913/ (Publisher URL)
PURE Output Type: Article
Published Date: 2018-01
Published Online Date: 2018-01-09
Accepted Date: 2017-12-11
Authors: Rice, Iain
Lowe, David



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record