Adaptive coherence conditioning

Abstract

Recently there has been much interest in design of systems to manage signal and noise environments adaptively with resource strategies that are optimized for detection performance. These approaches are particularly important for scenarios where the noise environment can change and therefore affect the amount of resources necessary for detection and estimation. A common way to manage these tradeoffs uses a min-max estimation strategy to handle the worst case signal and noise distribution and set resources and detection thresholds accordingly. In many of these approaches however, the difficulty of setting the number of resources to achieve the min-max bound for the worst case probability are difficult to gauge. We propose an approach that considers resource allocation as a problem in sparse approximation. The idea is to measure the current probability distribution and adapt to stay within the worst case bound while using the minimum number of resources necessary.