Claremont Center for the Mathematical Sciences

Traditional signal processing schemes sample signals at a high rate and immediately discard most of the information during the compression process. Compressed sensing is a new field that improves this by directly sensing the signal in compressed form using few nonadaptive, linear measurements. Adaptive sensing, which allows the selection of the next measurement based on previous observations, significantly improves signal recovery when arbitrary linear measurements can be constructed. However, in practice, the types of measurements that can be acquired are limited. In this talk, we will discuss recent results on the limitations and advantages of adaptive sensing when the measurements are constrained to belong to a finite set of allowable measurement vectors.