ToA-TS: Time of arrival based joint time synchronization and tracking for mobile underwater systems

Abstract

Time synchronization and localization are key requirements for distributed underwater systems consisting of numerous low-cost submersibles. In these systems, submersibles are highly resource constrained and typically have limited acoustic communication capability. We investigate the problem of tracking submersibles that only have the capability of receiving acoustic signals. Traditional Long Base Line (LBL) systems track the location of submersibles by providing a GPS-like infrastructure that consists of a few reference beacons at known locations. In these systems the unknown positions of submersibles are estimated from beacon transmissions using time-di↵erence-of-arrival (TDoA) based localization. As such TDoA makes the key assumption that beacon transmissions occur nearly concurrently in time. While this assumption is ensured in small LBL deployments it does not hold as the size of the system scales up. In this paper we identify scenarios where signals from multiple beacons are significantly lagged in time. We further identify the motion of the submersible between signal arrivals as a key factor that deteriorates the performance of TDoA, when transmissions are not concurrent. To address this problem we propose to track the submersible while performing time-synchronization. Our proposed technique,

A Review of Underwater Localization Techniques, Algorithms, and Challenges

“…In ToA-based systems, the target must be symmetric with anchor sensor nodes. The author's proposed a new model [79] called ToA-Based Tracked Synchronization (ToA-TS) expands GPS as localization to outline where beacon signals do not coincide and monitor the submersible while synchronizing time. And the beacon that transmits a signal will include the beacon's location data gathered from GPS, as well as the time stamp that represents the time it was sent based on worldwide time.…”

Compensation of asynchronous time of flight measurements in a long baseline navigation

“…We are about to determine the entries of (k) in (15) in the sense of minimum sensors, i.e. the least numbers and kinds of sensors required by the system to be observable.…”

“…In [12][13][14], the AUV processes the ToFs are in a sequence with known queuing times. On the other hand, in [15] this uncertainty is a consequence of the slow acoustic wave propagation speed that occurs in a scenario where a LBL with 10 3 m 2 order of area is being used by an AUV with a considerable fast speed.…”

“…Here, we model the state estimator while considering range bias that occurs in each ToF. Similar to [15], we also consider the clock offset as the source of range uncertainty while set a departure by addressing it now as a time-varying case. Once we obtain the systems state space representation, we apply the Kalman filter to estimate the AUV position and speed.…”

Ambient noise correlations on a mobile, deformable array

“…Since the AUEs can move in the period that the acoustic signals are being received, we can either assume that they are stationary during the buoy signal acquisition or we can estimate their motion during the signal reception by employing a factor graph framework. 19,20 We have performed both methods for this dataset and they yield similar trajectories. These estimated trajectories are used throughout the analysis given in this paper.…”