Sparse reconstruction of multi-window time-frequency representation based on Hermite functions

Branka Jokanović; Moeness G. Amin; Yimin D. Zhang

doi:10.1117/12.2050797

23 May 2014 Sparse reconstruction of multi-window time-frequency representation based on Hermite functions

Branka Jokanović, Moeness G. Amin, Yimin D. Zhang

Proceedings Volume 9109, Compressive Sensing III; 910908 (2014) https://doi.org/10.1117/12.2050797
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

Multi-window spectrograms offer higher energy concentration in contrast to the traditional single-window spec- trograms. However, these quadratic time-frequency distributions were not introduced to deal with randomly undersampled signals. This paper applies sparse reconstruction techniques to provide time-frequency represen- tations of nonstationary signals using the Hermite functions as multiple windows, under randomly sampled or missing data. The multi-window sparse reconstruction approach improves energy concentration by utilizing the common local sparse frequency support property across the different employed windows.

Citation Download Citation

Branka Jokanović, Moeness G. Amin, and Yimin D. Zhang "Sparse reconstruction of multi-window time-frequency representation based on Hermite functions", Proc. SPIE 9109, Compressive Sensing III, 910908 (23 May 2014); https://doi.org/10.1117/12.2050797

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