Real-time classification of buried targets with teams of unmanned vehicles

Recent rapid developments in autonomous underwater vehicle (AUV) technology have provided the opportunity to explore new approaches for detecting and classifying mine-like objects. In particular, the mobility of the vehicles the spatial diversity of the target scattering can be of advantage. The multi-platform approach can also lead to detection and classification algorithms that require significantly less computation than traditional sonar techniques, and as such these algorithms are more readily implementable in real-time onboard the vehicles. A method of target classification is shown in which the 3D scattered field is sampled by several receiver vehicles and information is extracted about the targets that clearly distinguish mines from rocks and rounded objects from oblong objects. The method is applicable to both buried and proud targets, and does not require the sub-wavelength accuracy navigation that is necessary for synthetic aperture sonar (SAS) imaging. The proposed classification method is shown to be easily implementable in real-time, as is demonstrated both in simulations and in post-processing experimental data from the 1998 generic oceanographic array technology sonar project (GOATS'98) experiment. Experimental data from the GOATS 2002 experiment are also presented.