Environmentally sensitive sparse gridding for efficient transmission loss calculations

Many tactical decision aids available to the Navy require acoustic sensor performance predictions over large ocean areas. Unfortunately, accurate computation of such acoustic fields requires significant computation time, which often renders the advanced technology tactically useless. The use of low-density uniform grids usually leads to unacceptable field uncertainty. Attempts to optimize grid density by analyzing the environment prior to acoustic calculation have made only modest gains, but tactical needs dictate an order of magnitude reduction. Further, in many cases, the relations between the desired accuracy, the choice of propagation model input parameters, and the required computation times are simply unknown. In this work, an iterative procedure is described to objectively determine a sparse, non-uniform grid that will maximize transmission loss accuracy as a function of computation time. The premise is that the physical environmental complexity controls the need for dense sampling in space and azimuth, and that the transmission loss curves already calculated or nearby coordinates on previous iterations can be used to predict that complexity. Although the grid grows unevenly, it is built around bookkeeping schemes commonly used for interpolation, so rapidly transforming the sparse grid to a uniform grid is always easy. Benchmarking against uniform grids shows that the total sparse grid field uncertainty is equal to or significantly lower than the uncertainty of uniform grids requiring similar computation time, and the method requires no a priori knowledge of the environment. Each iteration produces an acoustic field for the entire area of interest with ever-increasing accuracy, so the process can, in principle, be terminated whenever the gain in accuracy is overshadowed by the danger of further delay.