Publication Type:

Conference Proceedings





boundary estimation, Kalman filter, Nonparametric Regression, Range Sensors


We examine the problem of tracking dynamic boundaries occurring in natural phenomena using a network of range sensors. Two main challenges of the boundary tracking problem are accurate boundary estimation from noisy observations and continuous tracking of the boundary. We propose Dynamic Boundary Tacking (DBTR), an algorithm that combines the spatial estimation and temporal estimation techniques to effectively track a dynamic boundary. The regression-based spatial estimation technique determines discrete points on the boundary and estimates a confidence band around the entire boundary. In addition, a Kalman Filter-based temporal estimation technique tracks changes in the boundary and aperiodically updates the spatial estimation to meet accuracy requirements. DBTR, provides a low communication overhead solution to track boundaries without requiring prior knowledge about the dynamics. Experimental results demonstrate the effectiveness of our algorithm; estimated confidence bands indicate a loss of coverage of less than 2 − 5% for a variety of boundaries with different spatial characteristics.

Cite this Research Publication

S. Duttagupta, Ramamritham, K., and Kulkarni, P., “Tracking Dynamic Fronts using Sensor Network”. 2009.