Acoustic sensitivity analysis of optimal asset allocation for ASW

Abstract

In 1946, Koopman [Operations Evaluation Group Report 56, Navy Department] characterized optimal search for a stationary target in terms of arbitrary space-time effort density, with the searcher’s effectiveness defined by cumulative detection probability (given by the exponential search formula). Koopman’s solution maximizes target exposure at time t, given failure to detect at all previous times. This plan is myopic because it does not look into the future. In 1980, Brown [Operations Research 28, 1275–1289] generalized this result for a moving target. Brown’s algorithm allocates effort to maximize target exposure, given failure to detect by all prior and future search efforts. It converges to an optimal allocation, and typically converges rapidly because it simplifies sensor performance to a single factor (sweep rate in each cell), which eliminates the need for detailed sensor performance predictions. Experience has shown that sweep rate is an excellent predictor of search effectiveness, for high-level analyses. This work describes an approach to optimize several searchers simultaneously and investigates optimal asset allocation for coordinated, multiship missions under detailed conditions. The results show that attention to acoustic details can significantly increase overall search effectiveness, especially when using sensors with different capabilities. [Work sponsored by ONR.]