Controlled Sensing for Sequential Multihypothesis Testing
The problem of multiple hypothesis testing with observation control is considered in the sequential setting. In the case of uniform sensing cost, a test based on earlier work by Chernoff for binary hypothesis testing is shown to be first-order asymptotically optimal for multihypothesis testing in a strong sense, using the notion of decision making risk in place of the overall probability of error. Then, a new model for controlled sensing for sequential multihypothesis testing is proposed. This new model generalizes the existing model in two aspects. First, it includes a more complicated memory structure in the controlled observations. Second, it allows for a general cost that can depend on the realizations of both the observations and control values. Consequently, this new model is relevant to a wider class of applications, particularly in distributed and mobile sensor networks. A sequential test is proposed for this new model and is shown to be asymptotically optimal. Furthermore, the control policy associated with our asymptotically optimal test is shown be a self-tuning control policy that asymptotically achieves certain maximum rewards under all hypotheses simultaneously. (This is joint work with Dr. George Atia and Professor Venugopal V. Veeravalli.)
Speaker
Sirin Nitinawarat obtained the B.S.E.E. degree from Chulalongkorn University, Bangkok, Thailand, with first class honors, and the M.S.E.E. degree from the University of Wisconsin, Madison. He received his Ph.D. degree from the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park, in December 2010. He is now a postdoctoral research associate at the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His research interests are in stochastic control, information and coding theory, statistical signal processing, estimation and detection, communications, and machine learning. Dr. Nitinawarat was a co-organizer for the special session on "Controlled Sensing for Inference" at the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); and chair for the session on "Distributed Inference in Sensor Networks" at the 49th Annual Allerton Conference on Communication, Control, and Computing (2011). He was a finalist for the best student-paper award at the IEEE International Symposium on Information Theory, which was held at Austin, Texas, in 2010. During his Ph.D. study at the University of Maryland, he received graduate teaching fellowships in Fall 2007, Spring 2008, and Spring 2009.