Conventional electro-optical and infrared (EO/IR) systems capture an image by measuring the light incident at each of
the millions of pixels in a focal plane array. Compressive sensing (CS) involves capturing a smaller number of
unconventional measurements from the scene, and then using a companion process known as sparse reconstruction to
recover the image as if a fully populated array that satisfies the Nyquist criteria was used. Therefore, CS operates under
the assumption that signal acquisition and data compression can be accomplished simultaneously. CS has the potential
to acquire an image with equivalent information content to a large format array while using smaller, cheaper, and lower
bandwidth components. However, the benefits of CS do not come without compromise. The CS architecture chosen
must effectively balance between physical considerations (SWaP-C), reconstruction accuracy, and reconstruction speed
to meet operational requirements.
To properly assess the value of such systems, it is necessary to fully characterize the image quality, including artifacts
and sensitivity to noise. Imagery of the two-handheld object target set at range was collected using a passive SWIR
single-pixel CS camera for various ranges, mirror resolution, and number of processed measurements. Human
perception experiments were performed to determine the identification performance within the trade space. The
performance of the nonlinear CS camera was modeled with the Night Vision Integrated Performance Model (NV-IPM)
by mapping the nonlinear degradations to an equivalent linear shift invariant model. Finally, the limitations of CS
modeling techniques will be discussed.