Modelled sonar and target depth distributions for active sonar operations in realistic environments

Abstract

Target detection performance of mid-frequency active sonars depends heavily on both sonar and target depth. For a given target depth, a sonar performance model may help predict a sonar depth that maximizes the detection performance in the present environment. Similarly, for a given sonar depth, an optimal target depth to minimize the detection performance of the opposing sonar, may be predicted. Statistical representations of sonar and target behavior are required as prerequisites for Monte Carlo simulations, in which sonar and target depth are key parameters. In a real sonar operation, the choice of each depth parameter is subject to careful consideration of the current environment by qualified personnel. The a priori probability distributions from which the Monte Carlo method samples sonar and target depth therefore require sufficient realism to ensure the quality of the simulations. Here we propose an algorithm for generating distributions of sonar depth and the depth of an adversarial target in a realistic environment, based on calculating the Nash equilibrium strategies of the two parties, with the inputted probability of detection modelled by an acoustic ray tracer, Lybin. We demonstrate the proposed method in a sample environment and show the superior performance when compared to simpler distributions.