Partitioned Compressive Sensing With Neighbor-Weighted Decoding

Compressive sensing has gained momentum in recent years as an exciting new theory in signal processing with several useful applications. It states that signals known to have a sparse representation may be encoded and later reconstructed using a small number of measurements, approximately proportional to the signal's sparsity rather than its size. This paper addresses a critical problem that arises when scaling compressive sensing to signals of large length: that the time required for decoding becomes prohibitively long, and that decoding is not easily parallelized. The authors describe a method for partitioned compressive sensing, by which they divide a large signal into smaller blocks that may be decoded in parallel.

Provided by: Harvard University Topic: Mobility Date Added: Aug 2011 Format: PDF

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