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Neural Network Parameter Estimation for the Modified Bistatic Scattering Strength Model (BISSM)
Final technical rept.
NAVAL OCEANOGRAPHIC AND ATMOSPHERIC RESEARCH LAB STENNIS SPACE CENTER MS
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This technical note investigates the estimation of environmental parameters in the Bistatic Scattering Strength Model BISSM given backscatter strength and bathymetric data. A monostatic version of the model is derived, since this will be the form of data provided by acoustic imaging sensors. Feedforward neural networks, using the backpropagation learning algorithm, are used to perform the estimation of parameters for the nonlinear BISSM equation. The parameters that can be estimated are identified, and neural networks have been developed to estimate these parameters. Using noise-free artificial data generated with the BISSM equation, the networks provided excellent estimates of the desired parameters. The primary impetus for this work is a need for the Naval Oceanographic Office NAVOCENO to provide relevant survey support for Low-Frequency Active Acoustics LFAA programs and future Low-Frequency Active LFA operational systems. It has been recognized by the Commander, Naval Oceanographic Command CNOC that such support will require knowledge of certain bottom and subbottom properties and high-resolution geomorphology. The BISSM model has been proposed as a model for aspects of active bottom reverberation.
APPROVED FOR PUBLIC RELEASE