Modeling passive millimeter wave imaging sensor performance for discriminating small watercraft.

Passive millimeter wave (pmmW) imagers are quickly becoming practical sensor candidates for military and nonmilitary tasks. Our focus was to adapt the Night Vision [U.S. Army Research Development and Engineering Command, Communications and Electronics Research Development and Engineering Center, Night Vision and Electronics Sensors Directorate (NVESD)] passive thermal infrared imager performance models and apply them to pmmW imaging systems for prediction of field performance for the task of small watercraft and boat identification. The Night Vision Lab's infrared sensor model has been evolving since the 1950s, with the most current model being NVThermIP [Night Vision Thermal and Image Processing (NVThermIP) Model Users Manual, Rev. 9 (U.S. Army RDECON, CERDEC, NVESD, 2006)]. It has wide recognition as an engineering tool for sensor evaluation. This effort included collecting pmmW signatures for a representative set of targets, conducting an observer perception experiment, and deriving the task difficulty criteria that can be used in NVThermIP for identification of boats. The task difficulty criteria are used by designers and managers to create systems capable of meeting specific performance criteria in the field.

Millimeter-wave compressive holography.

We describe an active millimeter-wave holographic imaging system that uses compressive measurements for three-dimensional (3D) tomographic object estimation. Our system records a two-dimensional (2D) digitized Gabor hologram by translating a single pixel incoherent receiver. Two approaches for compressive measurement are undertaken: nonlinear inversion of a 2D Gabor hologram for 3D object estimation and nonlinear inversion of a randomly subsampled Gabor hologram for 3D object estimation. The object estimation algorithm minimizes a convex quadratic problem using total variation (TV) regularization for 3D object estimation. We compare object reconstructions using linear backpropagation and TV minimization, and we present simulated and experimental reconstructions from both compressive measurement strategies. In contrast with backpropagation, which estimates the 3D electromagnetic field, TV minimization estimates the 3D object that produces the field. Despite undersampling, range resolution is consistent with the extent of the 3D object band volume.

RF imaging, architectures, and applications: introduction to the feature issue.

This Applied Optics feature issue spotlights developments in the emerging areas of millimeter, submillimeter, and terahertz imaging.