CS: Distributed Target Localization via Spatial Sparsity. A listing of the most recent papers / links found on this blog.

Volkan Cevher, Marco Duarte and Richard Baraniuk just released Distributed Target Localization via Spatial Sparsity. The abstract reads:

We propose an approximation framework for distributed target localization in sensor networks. We represent the unknown target positions on a location grid as a sparse vector, whose support encodes the multiple target locations. The location vector is linearly related to multiple sensor measurements through a sensing matrix, which can be locally estimated at each sensor. We show that we can successfully determine multiple target locations by using linear dimensionality-reducing projections of sensor measurements. The overall communication bandwidth requirement per sensor is logarithmic in the number of grid points and linear in the number of targets, ameliorating the communication requirements. Simulations results demonstrate the performance of the proposed framework.

Also, since the information left on this blog can be pretty much overwhelming, I have added a new page that I hope I will be able to update often. This new page lists all the most recent papers / links / blog posts listed on Nuit Blanche on the subject of compressive sensing or compressed sensing. It really is a cut and paste of the main articles so some of the wording might stay. Anyway, the page is at:

http://igorcarron.googlepages.com/cslisting

Credit Photo: Nasa/JPL-Caltech/University of Arizona. Phoenix Landing site, sol 8.