Abstract
Buried target classification is of paramount importance in mine countermeasures (MCM) applications. The Generic Oceanographic Array Technology Sonar (GOATS) project approaches this fundamental problem by exploiting the assets of autonomous underwater vehicles (AUVs). Recent developments in unmanned vehicle technology have opened the door for new sonar design concepts that may be applied to create alternative target detection and classification methods in complex environments. Adaptive vehicle behaviors provide the potential for the sonar system to adjust its geometry for optimal performance. The vehicles can also access very shallow water environments, which allows for more complete multi-aspect views of targets of interest than are possible with remote towed array systems. In this paper, it is demonstrated that the multi-platform system can provide improved detection and classification capability, while at the same time reducing the computational requirements. Data from prior GOATS experiments (1998 and 2000) are used to illustrate the effectiveness of the multi-platform sonar concepts, and real-time simulations are used to show the planned implementation of these techniques on-board the AUVs for GOATS’02.
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