Feasibility of low-power one-way travel-time inverted ultra-short baseline navigation

Abstract

Recent and underway development efforts promise to deliver long endurance and deep-diving autonomous underwater vehicles with the potential to persistently observe the deep (6000 m) ocean interior and sea floor over time scales of months to years. These assets and their shallow-diving (<;1000 m) predecessors navigate primarily by dead-reckoning between surfacing for GPS fixes, a paradigm that precludes their use in missions where science objectives call for precise navigation deep in the water column or near the deep sea floor. Coupled with a single autonomous surface vessel, one-way travel time inverted ultrashort baseline positioning (OWTT-iUSBL) offers a compelling, but presently unrealized, alternative to infrastructure-intensive external acoustic aiding. Such systems could provide navigation aiding to multiple underwater vehicles while retaining a level of autonomy and endurance for the system as a whole comparable to that of a solitary vehicle. While the concept of OWTT-iUSBL is not new, we argue that the maturity of acoustic modem technology combined with the emergence of very low-power precision timing and attitude sensors will make it possible to deploy OWTT-iUSBL systems on low-power underwater vehicles in the near term. This paper presents two analyses in support of this conjecture. First, we discuss the factors that govern the achievable accuracy of OWTT-iUSBL navigation and present single-fix error budgets for specific system configurations using representative commercially-available components. Second, we consider the impact of a specific low-power configuration on the endurance of a deepprofiling autonomous underwater glider. Our analyses suggest that a practically realizable OWTT-iUSBL system could provide navigational accuracy 1-2 orders of magnitude superior to that presently achievable using periodic ascents to acquire global positioning system (GPS), and, for sufficiently deep deployments, actually yield more near-bottom data despite reducing overall vehicle endurance.