Synchronous-clock one-way-travel-time acoustic navigation for underwater vehicles

Summary

Ryan M. Eustice, Hanumant Singh and Louis L. Whitcomb, Synchronous-clock one-way-travel-time acoustic navigation for underwater vehicles. Journal of Field Robotics, Special Issue on State of the Art in Maritime Autonomous Surface and Underwater Vehicles, 28(1):121-136, 2011.

Abstract

This paper reports the development and deployment of a synchronous-clock acoustic navigation system suitable for the simultaneous navigation of multiple underwater vehicles. Our navigation system is comprised of an acoustic modem-based communication and navigation system that allows for onboard navigational data to be broadcast as a data packet by a source node, and for all passively receiving nodes to be able to decode the data packet to obtain a one-way travel time (OWTT) pseudo-range measurement and navigational ephemeris data. The reported navigation method herein uses a surface ship acting as a single moving reference beacon to a fleet of passively listening underwater vehicles. All vehicles within acoustic range are able to concurrently measure their slant range to the reference beacon using the OWTT measurement methodology, and additionally receive transmission of reference beacon position using the modem data packet. The advantages of this type of navigation system are that it can: i) concurrently navigate multiple underwater vehicles within the vicinity of the surface ship, and ii) provide a bounded-error XY position measure that is commensurate to conventional moored long-baseline (LBL) navigation systems (i.e., O(1 m)), but unlike LBL, is not geographically restricted to a fixed-beacon network. We present results for two different field experiments using a two-node configuration consisting of a global positioning system equipped surface ship acting as a global navigation aid to a Doppler-aided autonomous underwater vehicle. In each experiment, vehicle position was independently corroborated by other standard navigation means. Results for a maximum-likelihood sensor fusion framework are reported.

Bibtex entry