Underwater Sonar Data Fusion Using an Eecient 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 multiple hypothesis tracking (MHT) algorithm in order to resolve data association and motion correspondence ambiguities thereby to construct a model of the observed environment 2]. In particular, we describe a new, computationally eecient implementation of the MHT algorithm originally reported in 3] and validate target models previously developed for air sonar. The technique fuses data by modeling the physics of underwater sonar and its interaction with diierent object features. We illustrate the approach in two dimensions with real acoustic data taken using a high-frequency (1.25 MHz) pencil-beam prooling sonar, manually positioned along trajectories which circumnavigate prismatic objects.