No abstract

A floating ice-buoy system equipped with GPS and acoustic modems provided communication and navigation to a Bluefin 21 Autonomous Underwater Vehicle (AUV) for under-ice operations in the Beaufort Sea, in March 2020. Acoustic modems were placed above and below the “Beaufort Lens,” at 30 and 90 m, where a data- and model-based autonomy selected which depth to use for transmitting messages to the AUV. Similarly, the AUV ran a depth adaptive path planning behavior to maintain acoustic communications with the ice buoy modems. The untethered mission showed a non-diverging navigation solution with the average error roughly 0.1% of the total 11 km traveled. This talk will summarize key metrics introduced for automated, on-the-fly processing of acoustic packets to navigate within a reliable acoustic channel and minimize navigation error. [Supported by Office of Naval Research.]

A novel performance metric to improve underwater digital acoustic communication, called Multipath Penalty (MPP), is proposed as an alternative to traditional signal-to-noise ratio (SNR) methods in the context of the Arctic Beaufort Sea. MPP and SNR are compared alongside a third performance metric, Minimum Achievable Error (MAE), which replicates the operation of a channel estimate-based decision feedback equalizer in an acoustic modem. The three metrics are then tested in a hardware-in-the-loop Virtual Ocean simulator for an autonomous undersea vehicle (AUV) communicating with a collaborator. Using field data of modem statistics obtained duringICEX20 and expanded data supplied by the simulator, calibration of the three metrics to modem packet success is evaluated, resulting in a proposed recalibration for MAE. The AUV’s ability to communicate when adaptively choosing its depth is analyzed above and below the Beaufort Lens, and settings for MPP’s engineering variables are obtained. The results show MPP generally improves reception and demodulation of acoustic transmissions over SNR by approximately 5% within an operational range of 8 km, while achieving similar results to the more robust metric MAE. MPP is an improved utility for underwater digital acoustic communication in both marine autonomy and as a tactical decision aid.

This talk will cover operational methods and results for vehicle navigation from the Ice Exercise in March 2020 (ICEX20), in the Beaufort Sea. For short range acoustic transmissions, the total ice cover and the double ducted environment co-create complex multipath uncertainty. We assumed that the horizontal group velocity between source and receiver was smoothly varying, and embedded a “Virtual Ocean” with a real-time ray tracing engine to predict the horizontal group velocity for range estimation. This model-aided psuedorange approach, which enabled a successful vehicle recovery, favored the least multipath possible such that psuedoranges tended to overestimate the GPS-derived range by roughly 10 m. In post-processing, we modify the group velocity estimation method to consider varying degrees of multipath and achieve a mean absolute error of roughly 4 plus or minus 4 meters, rivaling GPS performance. To our knowledge, these results are the first field experiment to demonstrate a real-time, model-based data processing to constrain ranging error for underwater navigation. [Work supported by ONR & the NDSEG fellowship.]