Controlled Sensing for Sequential Multihypothesis Testing

Event Status

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.)

Date and Time
May 1, 2013, All Day