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A. Jung, G. Tauböck, F. Hlawatsch:
"Compressive Nonstationary Spectral Estimation Using Parsimonious Random Sampling of the Ambiguity Function";
in: "Proc. IEEE SSP-09", IEEE Conference Proceedings, 2009, 642 - 645.

We propose a compressive estimator for the discrete Rihaczek spectrum
(RS) of a time-frequency sparse, underspread, nonstationary
random process. The new estimator uses a compressed sensing technique
to achieve a reduction of the number of measurements. The
measurements are randomly located samples of the ambiguity function
of the observed signal. We provide a bound on the mean-square
estimation error and demonstrate the performance of the estimator
by means of simulation results. The proposed RS estimator can also
be used for estimating the Wigner-Ville spectrum (WVS) since for
an underspread process the RS and WVS are almost equal.

Basis Pursuit, Compressed Sensing, Nonstationary Spectral Estimation, Rihaczek Spectrum, Wigner Ville Spectrum

http://publik.tuwien.ac.at/files/PubDat_177502.pdf

Project Head Franz Hlawatsch:
Signal and Information Processing in Science and Engineering - Statistische Inferenz