Sonar-based bottom estimation in UUVs adopting a multi-hypothesis extended Kalman filter

Abstract

High precision bottom estimation techniques in unmanned underwater vehicles (UUVs) are examined in this paper. Environment sensing is performed by a high-frequency pencil beam profiling sonar mounted on Roby2, a small prototype UUV developed at CNR Istituto Automazione Navale. A multi-hypothesis extended Kalman filter to estimate the bottom slope and its distance from the vehicle is presented. Results obtained by applying this algorithm to real data collected with the vehicle moving in a high-diving pool are discussed. Algorithm improvements based on active sensing and "focusing attention" techniques are suggested.