TY - GEN
T1 - Joint recovery of sparse signals and parameter perturbations with parameterized measurement models
AU - Johnson, Erik C.
AU - Jones, Douglas L.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - Many applications involve sparse signals with unknown, continuous parameters; a common example is a signal consisting of several sinusoids of unknown frequency. Applying compressed sensing techniques to these signals requires a highly oversampled dictionary for good approximation, but these dictionaries violate the RIP conditions and produce inconsistent results. We consider recovering both a sparse vector and parameter perturbations from an initial set of parameters. Joint recovery allows for accurate reconstructions without highly oversampled dictionaries. Our algorithm for sparse recovery solves a series of linearized subproblems. Recovery error for noiseless simulated measurements is near zero for coarse dictionaries, but increases with the oversampling. This technique is also used to reconstruct Radio Frequency data. The algorithm estimates sharp peaks and transmitter frequencies, demonstrating the potential practical use of the algorithm on real data.
AB - Many applications involve sparse signals with unknown, continuous parameters; a common example is a signal consisting of several sinusoids of unknown frequency. Applying compressed sensing techniques to these signals requires a highly oversampled dictionary for good approximation, but these dictionaries violate the RIP conditions and produce inconsistent results. We consider recovering both a sparse vector and parameter perturbations from an initial set of parameters. Joint recovery allows for accurate reconstructions without highly oversampled dictionaries. Our algorithm for sparse recovery solves a series of linearized subproblems. Recovery error for noiseless simulated measurements is near zero for coarse dictionaries, but increases with the oversampling. This technique is also used to reconstruct Radio Frequency data. The algorithm estimates sharp peaks and transmitter frequencies, demonstrating the potential practical use of the algorithm on real data.
KW - Frequency Estimation
KW - Parameter Perturbations
KW - Parameterized Model
KW - Sparse Reconstruction
KW - Sparse Signal
UR - http://www.scopus.com/inward/record.url?scp=84890520118&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890520118&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638796
DO - 10.1109/ICASSP.2013.6638796
M3 - Conference contribution
AN - SCOPUS:84890520118
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5900
EP - 5904
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -