Motion-Compensated Acoustic Localization for Underwater Vehicles

Abstract

This paper develops and applies a Bayesian inference algorithm for long-baseline (LBL) acoustic localization of a maneuvering undersea vehicle which compensates for vehicle motion during the interrogation–reception time interval between the vehicle and transponders of the LBL system using only acoustic timing measurements. The method is based on including travel-time corrections as additional unknown parameters in the inference, constrained with prior estimates determined by interpolating the vehicle location at interrogation instants from the results of an initial localization based on a static-vehicle model. Monte Carlo simulation studies show that the motion-compensated localization method performs much better than the static-model localization method or a localization based on applying fixed travel-time corrections. Results from field trials carried out in a large lake are also presented, with averaged acoustic localization errors for a surface vehicle (judged relative to high-precision GPS locations) reduced from $19 \pm 14$ cm for the static-model localization to $3.4 \pm 1.4$ cm for the motion-compensated localization, which is comparable to the GPS uncertainties.