Towards evolutionary optimisation for high resolution bathymetry from sidescan sonars

The main objective of this paper is to use genetic algorithms in order to improve the quality of the bathymetry derived from sidescan raw data. The optimisation sequence starts with inverse modelling of the phase data, which uniquely corresponds to the characteristics of the coupled system of the sidescan vehicle and the seafloor terrain. These phase data are then compared with phase data actually collected by the sonar, to produce a correlation coefficient as an objective function. Simulation results are reported for the algorithm showing robust convergence towards the optimum value of the objective function. The results indicate that this new approach can be used to avoid difficulties widely encountered during forward processing of phase data to derive bathymetry