Sonar signal processing using tangent clusters

Describes a novel approach to the extraction of geometric features from sonic data. As is well known, a single measurement using a standard POLAROID sensor, though yielding relatively accurate information regarding the range of a reflective surface patch, provides scant information about the location in azimuth or elevation of that patch. This lack of sufficiently precise localization of the reflective patch hampers any attempt at data association, clustering of multiple measurements or subsequent classification and inference. The problem is particularly apparent in uncertain environments with unknown geometry, such as is found underwater. Moreover, the underwater environment precludes the usual (office-environment) simplification of two-dimensionality. The authors propose a multi-stage approach to clustering which aggregates sonic data accumulated from arbitrary transducer locations in an on-line fashion. It is computationally tractable and efficient despite the inherent exponential nature of clustering, and is robust in the face of noise in the measurements. It therefore lends itself to applications where the transducers are fixed relative to the mobile platform, where remaining stationary during a scan is both impractical and infeasible, and where deadreckoning errors can be substantial. The approach may be used both for map-building during exploration and for feature identification during navigation