search

UMD     This Site





On May 18, Assistant Professor Nuno Martins (ECE/ISR) gave an invited lecture at the Cymer Center for Control Systems and Dynamics at the University of California, San Diego. He spoke on "Certifying the optimality of decentralized state-estimation policies, in the presence of communication costs."

The talk focused on the design of optimal decentralized state estimators in the presence of communication costs. Dr. Martins discussed a method based on the theory of majorization, which allowed his team to prove, for the first time, that threshold policies at the sensors and Kalman-like estimators are jointly optimal for certain decentralized state estimation problems with communication costs. Theory of majorization has been widely used in statistics and more recently in network design to prove the optimality of certain registration policies. The lecture illustrated and advocated the use of majorization theory to certify the optimality of decentralized state estimation policies. This is joint work with Mr. G. Lipsa who is a Ph.D. candidate at the University of Maryland, College Park.

The Cymer Center Director is Miroslav Krstic. Before moving to UCSD, Krstic was a professor in the University of Maryland's Mechanical Engineering Department and an affiliated faculty member of ISR. Krstic now holds the Harold Sorenson Chair Professorship in the Department of Mechanical and Aerospace Engineering at UCSD.



May 13, 2009


«Previous Story  

 

 

Current Headlines

NEES in DOE EFRC Newsletter

Clark School team wins AFRL funding for swarm autonomy planning and metareasoning

George Dieter Celebrates His 90th Birthday, Applauds Social Change Course

Biodegradable color pixels that disappear completely

Alum Chao Wu wins public office in Howard County

A Fearless Flight

Stop—hey, what’s that sound?

Alumnus Yan Sun named IEEE Fellow

"Super Wood" wins multiple awards

Nationwide Urban Flooding Disrupts Local Economies, Public Safety, and Housing Equity

 
 
Back to top  
Home Clark School Home UMD Home