A long-term goal for checked baggage screening in airports has been to include passenger information, or at least a predetermined passenger risk level, in the screening process. One method for including that information could be treating the checked baggage screening process as a system-of-systems. This would allow for an optimized policy builder, such as one trained using the methodology of partially observable Markov decision processes (POMDP), to navigate the different sensors available for screening. In this paper we describe the necessary steps to tailor a POMDP for baggage screening, as well as results of simulations for specific screening scenarios.
For many years, the basic goal of sparse aperture design has been to maximize the support of the
modulation transfer function (MTF). Golay apertures and related nonredundant arrays are typically
used to achieve this objective. Unfortunately, maximizing the support of the MTF has the necessary
effect of decreasing the magnitude of the MTF at mid-band spatial frequencies. Fienup has shown
that the decreased magnitude of the MTF for nonredundant arrays contributes as much as reduced
throughput to the loss of SNR in sparse apertures relative to full aperture systems. This paper
considers the use of periodic sparse arrays to improve the mid-band MTF at the cost of reduced
spatial frequency coverage. We further consider methods to recover lost spatial frequencies using
multispectral and multiframe sampling and decompressive inference.
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