@inproceedings{6eaf5e14e490428496176ff34a6324e6,

title = "Detecting signal structure from randomly-sampled data",

abstract = "Recent theoretical results in Compressive Sensing (CS) show that sparse (or compressible) signals can be accurately reconstructed from a reduced set of linear measurements in the form of projections onto random vectors. The associated reconstruction consists of a nonlinear optimization that requires knowledge of the actual projection vectors. This work demonstrates that random time samples of a data stream could be used to identify certain signal features, even when no time reference is available. Since random sampling suppresses aliasing, a small (sub-Nyquist) set of samples can represent high-bandwidth signals. Simulations were carried out to explore the utility of such a procedure for detecting and classifying signals of interest.",

author = "Boyle, {Frank A.} and Jarvis Haupt and Fudge, {Gerald L.} and Yeh, {Chen Chu A.}",

year = "2007",

doi = "10.1109/SSP.2007.4301273",

language = "English (US)",

isbn = "142441198X",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

pages = "326--330",

booktitle = "2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings",

note = "2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 ; Conference date: 26-08-2007 Through 29-08-2007",

}