UUV localization using acoustic communications, networking, and a priori knowledge of the ocean current

Underwater navigation is particularly challenging due to the fact that a number of navigation aids, such as GPS and similar, are not available. In order to accurately estimate a UUV's position at any time during a mission, we are relying on acoustic communication between the UUV and a network of surface platforms at known locations (reference points). By using the acoustic modems and a model of the environment, the acoustic wave travel time from the UUV to the reference points can be measured and converted into a distance. These distance measurements are then used by a tracking algorithm to improve the UUV positioning accuracy. In previous work [1], a tracking algorithm based on the Unscented Kalman Filter (UKF) was developed presenting satisfactory results. As part of the UUV tracking model, the drift caused by the ocean current was modeled as a random walk and is part of the state of the system. Based on predictions for the ocean current made by different UUVs at different times, a consensus algorithm was developed [2]. The knowledge of the ocean current provided by the consensus algorithm is then used to improve the UUV positioning. The developed algorithms were initially tested using synthetic data. To validate the simulation results, the algorithms were applied to data collected during sea tests that took place in Monterey Bay in August, 2015.

[1]  Matthew J. Hahn.  Undersea navigation via a distributed acoustic communications network , 2005 .

[2]  Renato P Vio,et al.  Improved UUV Positioning Using Acoustic Communications and a Potential for Real-Time Networking and Collaboration , 2017 .

[3]  Peter I. Corke,et al.  Further Results with Localization and Mapping Using Range from Radio , 2005, FSR.

[4]  G. Clifford Carter,et al.  Coherence and time delay estimation : an applied tutorial for research, development, test, and evaluation engineers , 1993 .

[5]  P. H. Milne,et al.  Underwater Acoustic Positioning Systems , 1983 .

[6]  W. Marsden.  I and J , 2012 .

[7]  W. Kuperman,et al.  Computational Ocean Acoustics , 1994 .

[8]  Michalopoulou.  Matched-impulse-response processing for shallow-water localization and geoacoustic inversion , 2000, The Journal of the Acoustical Society of America.

[9]  Richard Bellman,et al.  Introduction to Matrix Analysis , 1972 .

[10]  J.J. Leonard,et al.  Experimental validation of the moving long base-line navigation concept , 2004, 2004 IEEE/OES Autonomous Underwater Vehicles (IEEE Cat. No.04CH37578).

[11]  G. Carter,et al.  The generalized correlation method for estimation of time delay , 1976 .

[12]  J. Ianniello,et al.  Time delay estimation via cross-correlation in the presence of large estimation errors , 1982 .

[13]  Roberto Cristi,et al.  Near real-time improved UUV positioning through channel estimation - the Unscented Kalman Filter approach , 2016, OCEANS 2016 MTS/IEEE Monterey.

[14]  M. Porter,et al.  Gaussian beam tracing for computing ocean acoustic fields , 1987 .