A model based system for simultaneously estimating bathymetry and sound speed profile characteristics - non-isovelocity simulation results

Presents further developments of an adaptive bathymetric estimation (ABE) algorithm for use with forward-looking bathymetric sonar in non-isovelocity underwater acoustic environments. In addition to providing improved positional estimates of ocean bottom contacts in front of the host vehicle, it will automatically estimate and adapt to changes in the local sound speed profile (SSP). The ABE algorithm uses parametric SSP models of increasing complexity and tunes them "on the fly" to match the observed refraction of the sonar bottom returns. The technique is an application of the Extended Kalman Filter (EKF) which fuses on-board navigational data of the vessel (from GPS or INS system), multiple active forward-looking sonar returns from bottom contacts (time of arrival and angle of arrival estimates), and underwater sound propagation parameter estimates derived from an internal eigenray model. This paper shows simulation for three scenarios using increasingly sophisticated ABE models: (1) discrete bottom sonar observations refracted by a linear sound speed gradients, (2) the same observations refracted by a 2nd order gradient, and (3) tracking changes in a mean sound speed over a featureless (smooth) bottom model.