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Bistatic/Multistatic Scattering for Autonomous Acoustic Target Characterization

Funding Agencies

ONR

Partners/Collaborators

MIT

Research Papers

  1. E. M. Fischell and H. Schmidt. “Supervised machine learning for estimation of target aspect angle from bistatic acoustic scattering.” IEEE JOE (2017). Doi: 10.1109/JOE.2017.2650759.

  2. E. M. Fischell and H. Schmidt. “Environmental effects on seabed object bistatic scattering classification.” J. Acoust. Soc. Am. 141, 28-37 (2017).

  3. E. M. Fischell and H. Schmidt. “AUV behaviors for collection of bistatic and multistatic acoustic scattering data from seabed targets.” 2016 IEEE International Conference on Robotics and Automation (ICRA), pp 2645-2650 (2016).

  4. E. M. Fischell and H. Schmidt, “Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields.” J. Acoust. Soc. Am. 138, 3773-3784 (2015).

  5. E. Fischell, T. Schneider and H. Schmidt, “Design, Implementation and Characterization of Precision Timing for Bistatic Acoustic Data Acquisition.” IEEE JOE, ISSN: 0364-9059 (2015).