Regularized Robust Estimation with

There are always discrepancies between design models and the actual physical systems or phenomena that they model. Regardless of their source, such perturbations can degrade the performance of otherwise optimal designs. This talk describes a design strategy for state-space models with bounded perturbations. In comparison to other robust formulations, the resulting procedure performs data regularization as opposed to de-regularization; a property that avoids continuous testing of existence conditions and is therefore attractive for online/real-time operation. An application in the context of mobile wearable computing is described. These devices are supposed to provide users with real-time access to information databases in a natural and unobtrusive manner, so much so that computing and sensing must be reliable and easy to interact with. This talk uses the developed theory to formulate a robust hand-gesture interface.