Underwater sonar data fusion using an efficient multiple hypothesis algorithm

This paper describes a geometric approach to underwater environmental modeling using sonar. We classify and localize geometric features of man-made objects by combining the boundary constraints of sonar returns obtained from multiple vantage points. The approach builds on our previous use of Reid's (1979) multiple hypothesis tracking (MHT) algorithm in order to resolve data association and motion correspondence ambiguities thereby to construct a model of the observed environment (Cox and Leonard, 1994). In particular we describe a new, computationally efficient implementation of the MHT algorithm originally reported in (Cox and Miller, 1995) and validate target models previously developed for air sonar. The technique fuses data by modeling the physics of underwater sonar and its interaction with different object features. We illustrate the approach in two dimensions with real acoustic data taken using a high-frequency (1.25 MHz) pencil-beam profiling sonar, manually positioned along trajectories which circumnavigate prismatic objects.