17,470 research outputs found
How to find real-world applications for compressive sensing
The potential of compressive sensing (CS) has spurred great interest in the
research community and is a fast growing area of research. However, research
translating CS theory into practical hardware and demonstrating clear and
significant benefits with this hardware over current, conventional imaging
techniques has been limited. This article helps researchers to find those niche
applications where the CS approach provides substantial gain over conventional
approaches by articulating lessons learned in finding one such application; sea
skimming missile detection. As a proof of concept, it is demonstrated that a
simplified CS missile detection architecture and algorithm provides comparable
results to the conventional imaging approach but using a smaller FPA. The
primary message is that all of the excitement surrounding CS is necessary and
appropriate for encouraging our creativity but we all must also take off our
"rose colored glasses" and critically judge our ideas, methods and results
relative to conventional imaging approaches.Comment: 10 page
Frequency-modulated continuous-wave LiDAR compressive depth-mapping
We present an inexpensive architecture for converting a frequency-modulated
continuous-wave LiDAR system into a compressive-sensing based depth-mapping
camera. Instead of raster scanning to obtain depth-maps, compressive sensing is
used to significantly reduce the number of measurements. Ideally, our approach
requires two difference detectors. % but can operate with only one at the cost
of doubling the number of measurments. Due to the large flux entering the
detectors, the signal amplification from heterodyne detection, and the effects
of background subtraction from compressive sensing, the system can obtain
higher signal-to-noise ratios over detector-array based schemes while scanning
a scene faster than is possible through raster-scanning. %Moreover, we show how
a single total-variation minimization and two fast least-squares minimizations,
instead of a single complex nonlinear minimization, can efficiently recover
high-resolution depth-maps with minimal computational overhead. Moreover, by
efficiently storing only data points from measurements of an
pixel scene, we can easily extract depths by solving only two linear equations
with efficient convex-optimization methods
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