Tracking Dynamic Boundary Fronts Using Range Sensors

We examine the problem of tracking dynamic boundaries occurring in natural phenomena using range sensors. Two main challenges of the boundary tracking problem are energy-efficient boundary estimations from noisy observations and continuous tracking of the boundary. We propose a novel approach which uses a regression-based

spatial estimation

technique to determine discrete points on the boundary and estimates a confidence band around the entire boundary. In addition, a Kalman Filter-based

temporal estimation

technique is used to selectively refresh the estimated boundary to meet the accuracy requirements. Our algorithm for dynamic boundary tracking (DBTR) combines

temporal

estimation with an aperiodically updated

spatial

estimation and provides a low overhead solution to track boundaries without requiring

prior

knowledge about the dynamics of the boundary. Experimental results demonstrate the effectiveness of our algorithm and estimated confidence bands achieve

loss of coverage

of less than 2 − 5% for a variety of boundaries with different spatial characteristics.