Multi-Dimensional Visual Representations for Underwater Environmental Uncertainty

Published in Institute of Electical and Electronics Engineers (IEEE) Computer Graphics and Applications, v24 n5 p56-65, Sep-Oct 2004.The original document contains color images.

Uncertain temperature-salinity depth profile information results in inaccurate prediction of the curvature of the acoustic rays. Uncertain bathymetry (knowledge of the ocean bottom) includes misregistered maps, data gaps in maps, temporal changes to the ocean bottom, and limited knowledge of the mineral content of the ocean bottom. These factors again limit the accuracy of position estimations based on the acoustic returns. Finally, local oceanic effects (e.g., internal waves, thermal currents, fine-structure) also impact the acoustic return and thus add to the uncertainty surrounding the target's position. The resulting uncertainty has four dimensions: latitude, longitude, depth, and signal strength. The target state prediction process (i.e. determining the position, heading, etc. of a target) involves data that spans multiple dimensions caused by the uncertainties discussed above. We investigate how to represent the resulting multivariate information and multi-dimensional uncertainty by developing and applying candidate visual techniques. At this time, our primary focus is to (1) develop the statistical characterizations for the environmental uncertainty (described only briefly in this paper) and (2) develop a visual method for each characterization. We received feedback on the applicability of our techniques from domain experts. We used this in conjunction with previous results to compile a set of development guidelines (some obvious, others not). We first review a select number of applicable visualization techniques. We then describe the investigations for representing the bathymetric information in Section 3 and the target state estimation uncertainty in Section 4. Section 5 describes briefly the display-system architecture we developed to demonstrate the visual candidates. Finally, in Section 6 we summarize the results and compile a list of guidelines for developing visualization techniques for multidimensional uncertainty and multi-variate information.