Environmental effects of the Beaufort Lens on underwater acoustic communications during Arctic operations

Operations in the Arctic Ocean are increasingly important due to the changing environment and the resulting global implications. These changes range from the availability of new global trade routes, accessibility of newly available resources in the area, and national security interests of the United States in the region. It’s necessary to build a greater understanding of the undersea environment and how it’s changing since these environmental changes have a direct impact on adjusting future operations in the region and looming global changes as less Arctic ice is present. The recent presence of the Beaufort Lens is changing the acoustic propagation paths throughout the Arctic region. Here a network of buoys were employed to communicate with an Autonomous Undersea Vehicle (AUV) while it operated under the ice throughout the Beaufort Lens with the goal of achieving near GPS quality navigation. The acoustic communications paths were compared using a vertical array throughout the Beaufort Lens. This beam forming was compared to the prediction from BELLHOP. As well, since acoustic communications are affected by multi-path, attenuation and interference from other sources it was interesting to note that bottom bounce was sometimes a reliable acoustic path.

Multi-path Penalty metric in underwater acoustic communication for autonomy and human decision-making

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.

Building Survivable Software Systems by Automatically Adapting to Sensor Changes

Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. If these software systems could automatically adapt to hardware changes, it would significantly reduce the maintenance cost and enable rapid upgrade. In this paper, we study the problem of how to automatically adapt to sensor changes, as an important step towards building such long-lived, survivable software systems. We address challenges in sensor adaptation when a set of sensors are replaced by new sensors. Our approach reconstructs sensor values of replaced sensors by preserving distributions of sensor values before and after the sensor change, thereby not warranting a change in higher-layer software. Compared to existing work, our approach has the following advantages: (a) ability to exploit new sensors without requiring an overlapping period of time between the new sensors and the old ones; (b) ability to provide an estimation of adaptation quality; and (c) ability to scale to a large number of sensors. Experiments on weather data and Unmanned Undersea Vehicle (UUV) data demonstrate that our approach can automatically adapt to sensor changes with 5.7% higher accuracy compared to baseline methods.

Self-Coupling Black Box Model of a Dynamic System Based on ANN and Its Application

The black box model of a dynamic system usually consists of just input and output. There is no correlation or coupling between the input and output. This paper proposes a self-coupling black box model method to realize the coupling between its input and output by introducing “virtual variables” to the black box model of a dynamic system considering the advantages of artificial neural network (ANN) in the system. The ANN is used for black box modeling. The modeling process of the self-coupling black box is illustrated through the simulation models and simulation analysis of the particle settlement process and the Unmanned Undersea Vehicle (UUV) launching process. By comparing its result with the result of the standard black box model, the advantages and disadvantages of the self-coupling and standard black box models in the calculation of accuracy are analyzed.

Undersea Vehicle Autopilot Off Weight Design Compensation

A service issue that can adversely affect the performance of many undersea vehicles in general application is either over or under weight operation. Depth keeping precision can be impacted when a vehicle is launched at a weight different from that specified in the nominal flight control system design. As a result, overall maneuvering performance and the vehicle application objectives can be significantly impacted. This paper presents a compensation method based on simple expansion of the vehicle’s autopilot depth controller trim schedule. Expansion is defined relative to a vehicle’s nominally fixed weight-buoyancy flight control equilibrium trim design point and refers to practical variances in both net buoyancy and buoyancy-weight center geometric offset. This implementation requires only a simple, highly feasible, dry dockside launch under/overweight measurement for operational flight static reference. Off weight compensation is enabled by a priori determination of the vehicle’s steady speed, straight-horizontal flight path, body pitch, and elevator trim angles when subjected to the expected set range of weight-buoyancy variations. The method and implementation are outlined. A depth step change maneuver, using a high fidelity autopilot-software-in-the-loop maneuvering simulation, is examined to verify the implementation feasibility and effectiveness.