Coherence gradients in adaptive risk estimation

Many sensing scenarios involve tradeoff between diverse sets of constraints that involve measuring and parameterizing some high dimensional sensing environment trade-space to give minimal estimation risk. Integrating and simplifying these tradeoffs is a daunting task, particularly in the context where the underlying assumptions of signal and noise distributions can result from complex statistical models and these models can evolve in time. Thus we need a strategy to reduce the complexity of the measurement process, parameterize the tradeoff space, and adapt to dynamic changes as the sensing scenarios evolve. We propose using coherence of the measurement process as a means of characterizing both the resource trade-space and the estimation process. We will use gradients in the coherence resource trade space to find the minimum estimator risk over some local sensing space and show how this process can adapt the process to global changes when they occur.