Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging.
In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.