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- W1997679570 abstract "A large portion of work on compressive sampling and sensing has focused on reconstructions from a given measurement set. When the individual samples are expensive and optional, as is the case with autonomous agents operating in a physical domain and under specific energy limits, the CS problem takes on a new aspect because the projection is column-sparse, and the number of samples is not necessarily large. As a result, random sampling may no longer be the best tactic. The underlying incoherence properties in l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> reconstruction, however, can still motivate the purposeful design of samples in planning for CS with one or more agents; we develop here a greedy and computationally tractable sampling rule that will improve errors relative to random points. Several example cases illustrate that the approach is effective and robust." @default.
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- W1997679570 date "2011-12-01" @default.
- W1997679570 modified "2023-09-25" @default.
- W1997679570 title "One-step-ahead kinematic compressive sensing" @default.
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- W1997679570 doi "https://doi.org/10.1109/glocomw.2011.6162400" @default.
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