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Greg Anderson

Principal Engineer

Email

gma@apl.washington.edu

Phone

206-543-4648

Research Interests

Sonar System Performance Modeling, Simulation, Optimization, Inversion, Statistics, Computational Intelligence

Biosketch

Gregory Anderson designs and develops tools for ocean sensor acoustic performance prediction, deployment optimization, and environmental parameter estimation (inversion), primarily for U.S. Navy anti-submarine warfare (ASW) tactical decision aids. His areas of expertise are system design, digital modeling and simulation, statistics, optimization, and parallel computing. Mr. Anderson joined the Laboratory's professional staff in 1990.

Education

B.S. Agriculture, University of Idaho, 1974

B.S. Applied Mathematics, University of Idaho, 1975

M.S. Electrical Engineering, University of Idaho, 1980

Publications

2000-present and while at APL-UW

3-D filter methods for sensor optimization

Krout, D.W., J. Hsieh, M. Antonelli, M. Hazen, and G.M. Anderson, "3-D filter methods for sensor optimization," U.S. Navy J. Underwater Acoust., 61, 137-148, 2011.

15 Jan 2011

An at-sea, autonomous, closed-loop concept study for detecting and tracking submerged objects

Stevenson, J.M., et al., including J. Luby, R.T. Miyamoto, M. Grund, G. Anderson, and M. Hazen, "An at-sea, autonomous, closed-loop concept study for detecting and tracking submerged objects," U.S. Navy J. Underwater Acoust., 59, 671-690, 2009.

1 Jun 2009

Distributed environmental inversion for multi-static sonar tracking

Pitton, J., A. Ganse, G. Anderson, and D.W. Krout, "Distributed environmental inversion for multi-static sonar tracking," Proc., 9th International Conference on Information Fusion, 10-13 July, Florence, Italy, 6 pp., doi:10.1109/ICIF.2006.301710 (IEEE, 2006).

More Info

10 Jul 2006

This paper presents an approach for adapting a tracking algorithm to the acoustic propagation environment. This adaptation is performed by incorporating the expected target signal-to-noise ratio (SNR) into the data association step through the measured contact amplitude. In this work, expected SNR is provided via acoustic modeling; estimates of bottom loss and scattering strength, required by the acoustic model, are obtained via inversion of the acoustic model based on measured multi-static sonar reverberation data. This paper shows that the use of distributed sensors provides improved estimates of the environmental parameters, and hence better estimates of the expected SNR.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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