Abstract
An application of a genetic algorithm and Monte Carlo simulation with Bayesian detection statistics is used to optimize sonar search tracks in nonhomogeneous environments. The optimization metric is maximum cumulative detection probability for a specific sonar (passive or active) against a target with specified characteristics (acoustic and tactical) during a fixed time period. This new search planning capability is named GRASP for genetic range-dependent algorithm for search planning. The sensitivity of GRASP solutions to various ocean environments is examined under benchmark conditions, i.e., fairly simple synthetic environments and a simple target model. The results indicate that the genetic algorithm produces intuitive tracks that correlate highly with acoustic signal excess predictions, as expected.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
