Compressive spectral estimation for nonstationary random processes

We propose a “compressive” estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal’s Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Alexander Jung, Georg Tauböck, Franz Hlawatsch
• Keywords: Compressive Wvs Estimator, ICASSP 2009, Signal Processing, Undersampled Time-frequency Grid, WVS Estimator