Underwater object tracking using electrical impedance tomography
|Title||Underwater object tracking using electrical impedance tomography|
|Publication Type||Conference Paper|
|Year of Publication||2012|
|Authors||Snyder, J. B., Y. Silverman, Y. Bai, and M. A. MacIver|
|Conference Name||2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012)|
|Conference Location||Vilamoura-Algarve, Portugal|
Few effective technologies exist for sensing in dark or murky underwater situations. For this reason, we have been exploring the use of a novel biologically-inspired approach to non-visual sensing based on the detection of perturbations to a self generated electric field. This is used by many species of neotropical nocturnal freshwater fish. This approach, termed active electrosense, presents unique challenges for sensing and tracking of nearby objects. We explore two methods for estimating the velocity of objects through active electrosense. The first of these methods uses a simple cross-correlation method, which depends on the uniformity of the electric field. We show some of the ramifications of making this assumption for a self-generated field around a cylindrical pod-shaped sensor in a rectangular tank. We then evaluate the use of methods developed for electrical impedance tomography (EIT) for localization and tracking. This is an unusual application of EIT in that typical applications involve surrounding the volume of interest (such as the thorax of humans) with sensor/emitters. Here, rather than this “outside in” approach, we are using EIT “inside out.” In simulation, we nonetheless find significant improvements in the accuracy of estimated velocity when using the EIT approach. Additionally, we demonstrate how EIT may be used for accurate position estimation. Under the conditions evaluated, the computation time for inversion is low enough to make its use feasible as a primary position and velocity estimator in an on- line system or as a secondary system to augment a computationally inexpensive estimator.