Near-Optimal Bayesian Localization via Incoherence and Sparsity

Source localization using a network of sensors is a classical problem with applications in tracking, habitat monitoring, etc. A small number of transmitters in the plane broadcast sine waves, a small number of receivers record the signal, and we want to recover the transmitters' locations from the receivers' observations. A solution to this estimation problem must satisfy a number of competing resource constraints, such as estimation accuracy, communication and energy costs, signal sampling requirements and computational complexity. This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network.

Joint work with Volkan Cevher (Rice), Petros Boufounos (Rice), Richard G. Baraniuk (Rice), and Anna C. Gilbert (Michigan)

Martin J. Strauss has an A.B. degree from Columbia University and a PhD from Rutgers University, both in mathematics. In 1996, he was a postdoctoral fellow at Iowa State University, from 1997 to 2004, he was a member of technical staff at AT&T Labs-Research in Florham Park, NJ, and he has been at the University of Michigan since then. His research interests include fundamental algorithms, especially randomized and approximation algorithms; algorithms for massive data sets; signal processing and and computational harmonic analysis; computer security and cryptography; complexity theory.