Underwater object tracking using electrical impedance tomography

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.

Authors

• James Snyder
• Yonatan Silverman
• Yang Bai
• Malcolm A. MacIver

Keywords

• Electrodes
• Kalman filters
• Tomography
• Conductivity
• Voltage measurement
• Estimation
• Robot sensing systems
• Impedance
• Electrical Impedance Tomography
• Position Estimation
• Velocity Estimation
• Cross-correlation Method
• Object Velocity
• Environmental Conditions
• Conductive
• Electrodeposition
• Kalman Filter
• Focal Length
• Major Axis
• Sensory Modalities
• Position Error
• Inverse Problem
• Jacobian Matrix
• Pair Of Electrodes
• 3D Mesh
• Object Position
• Uniform Field
• Environmental Boundaries
• Regularized Regression
• Geometry Of Space
• Kalman Filter For Estimation
• Element Mesh
• Cross-correlation Algorithm
• Current Source