3 research outputs found
Target Detection Performance Bounds in Compressive Imaging
This paper describes computationally efficient approaches and associated
theoretical performance guarantees for the detection of known targets and
anomalies from few projection measurements of the underlying signals. The
proposed approaches accommodate signals of different strengths contaminated by
a colored Gaussian background, and perform detection without reconstructing the
underlying signals from the observations. The theoretical performance bounds of
the target detector highlight fundamental tradeoffs among the number of
measurements collected, amount of background signal present, signal-to-noise
ratio, and similarity among potential targets coming from a known dictionary.
The anomaly detector is designed to control the number of false discoveries.
The proposed approach does not depend on a known sparse representation of
targets; rather, the theoretical performance bounds exploit the structure of a
known dictionary of targets and the distance preservation property of the
measurement matrix. Simulation experiments illustrate the practicality and
effectiveness of the proposed approaches.Comment: Submitted to the EURASIP Journal on Advances in Signal Processin