Rapid anomaly detection and tracking via compressive time-spectra measurement

The main goal of the Phase II program is to build and test an operational prototype compressive sensing (CS) camera capable of high-speed change detection. Based on the algorithmic development, hardware modeling and simulation achievements of Phase I, we have defined a real-time CS framework that will be implemented in Phase II in an operational prototype. This framework, similar to Phase I, begins with the high speed measurement capability of the CS platform and the programmability of the CS architecture that a digital micromirror array provides to implement the various measurement models. While data acquired in these models can be reconstructed into images to provide scene contextual information at selectable resolution, we concentrate in this proposal on the compressed domain data for high speed change detection based on the application of our Sum-to-One fast transform and Sparse-in-Time model statistics. We also investigate enhancement of these models using spectral domain measurement and unmixing techniques. In this proposal, we develop objectives for implementing the change detection computational framework in the CS spectral camera architecture developed in Phase I to achieve a Phase II high-speed change detection prototype that operates in the shortwave infrared.