Bayesian network modeling of acoustic sensor measurements

Control and optimization of acoustic sensors can significantly impact the effectiveness of sonar deployment in variable and uncertain underwater environments. On the other hand, the design of optimal control systems requires tractable models of system dynamics, which in this case include acoustic-wave propagation phenomena. High-fidelity acoustic models that capture the influence of environmental conditions on wave propagation involve partial differential equations (PDEs), and are computationally intensive. Also, by relying on the numerical solution of PDEs for given boundary and initial conditions, they do not provide closed-form functional forms for the propagation loss or other output variables. In this paper, a simple Bayesian network (BN) model of acoustic propagation is presented for use in sonar control. The performance of the BN model compares favorably to that of a radial basis function neural network. Additionally, the sensor range dependency on spatial and temporal coordinates can be estimated and utilized to compute optimal sonar control strategies.