Compressive sensing applications for single detector rosette scanning infrared seekers

Compressive sensing (CS) theory states that a signal which can be sparsely represented in a known basis may be reconstructed from its samples which have been obtained below the Nyquist rate. Image reconstruction with a single detector using CS theory has been shown to give promising results. In this work, we investigate the application of CS theory to single detector infrared (IR) rosette scanning systems. The single detector pseudo-imaging rosette scanning system scans the scene with a specific pattern and performs processing to estimate the target location without forming an image. These systems suffer from low performance compared to costly focal plane array (FPA) detectors. Using the CS framework, these scanning systems may be improved by reconstructing the samples obtained by the rosette scanning pattern. For this purpose, we consider surface to air engagement scenarios where the IR images contain aerial targets and flares. The IR images have been reconstructed from samples obtained with the rosette scanning pattern and other baseline sampling strategies. It has been shown that the proposed scheme exhibits good reconstruction performance and large size FPA imaging performance can be achieved using a single IR detector with a rosette scanning pattern.