A Sampling Theory for Mobile Sensing
Jayakrishnan Unnikrishnan, EPFL - LCAV

Seminar • 29 October 2012 • BM 4.233

Abstract
Consider the problem of sampling a bandlimited spatial field using mobile sensors. Classical sampling theory commonly uses the spatial density of samples as the performance metric of a sampling scheme. We argue that in the mobile sensing paradigm, a more relevant metric is the path density, or total distance traveled by the sensors per unit spatial volume. We introduce the problem of designing sampling trajectories with minimal path density subject to the constraint that bandlimited spatial fields can be perfectly reconstructed using samples taken on these trajectories. We obtain partial solutions to this problem from certain restricted classes of trajectories. Our results for trajectories can be generalized to results for higher dimensional sampling manifolds. In the last part of the talk we demonstrate the possibility of performing spatial anti-aliasing by simultaneously using mobile sensing and time domain filtering. Our results have applications in environment monitoring with mobile sensors, and also in designing scanning schemes for MRI.