Reconstruction of locally frequency sparse nonstationary signals from random samples. Amin, M., Jokanović, B., & Dogaru, T. In *2014 22nd European Signal Processing Conference (EUSIPCO)*, pages 1771-1775, Sep., 2014.

Paper abstract bibtex

Paper abstract bibtex

The local sparsity property of frequency modulated (FM) signals stems from their instantaneous narrowband characteristics. This enables their reconstruction from few random signal observations over a short-time window. It is shown that for linear FM signals, the sparsity of the local frequencies is equal to the window length, thus adding another specification to the window selection requirements, beside the conventional temporal and spectral resolutions. Stable signal reconstruction within a sliding window depends on the underlying probability distribution function guiding the random sampling intervals. Both simulations and computational EM modeling data are used to demonstrate the effectiveness of local reconstructions. We consider both mono-component FM signals and multi-component signals, corresponding to maneuvering targets and human gait Doppler signatures, respectively.

@InProceedings{6952654, author = {M. Amin and B. Jokanović and T. Dogaru}, booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)}, title = {Reconstruction of locally frequency sparse nonstationary signals from random samples}, year = {2014}, pages = {1771-1775}, abstract = {The local sparsity property of frequency modulated (FM) signals stems from their instantaneous narrowband characteristics. This enables their reconstruction from few random signal observations over a short-time window. It is shown that for linear FM signals, the sparsity of the local frequencies is equal to the window length, thus adding another specification to the window selection requirements, beside the conventional temporal and spectral resolutions. Stable signal reconstruction within a sliding window depends on the underlying probability distribution function guiding the random sampling intervals. Both simulations and computational EM modeling data are used to demonstrate the effectiveness of local reconstructions. We consider both mono-component FM signals and multi-component signals, corresponding to maneuvering targets and human gait Doppler signatures, respectively.}, keywords = {frequency modulation;signal reconstruction;signal sampling;signal reconstruction;locally frequency sparse nonstationary signals;random samples;local sparsity property;frequency modulated signals;instantaneous narrowband characteristics;random signal observations;short-time window;linear FM signals;window length;window selection;temporal resolutions;spectral resolutions;sliding window;probability distribution function;random sampling intervals;monocomponent FM signals;multicomponent signals;maneuvering targets;human gait Doppler signatures;Time-frequency analysis;Frequency modulation;Image reconstruction;Chirp;Radar imaging;Compressed sensing;Local sparsity;nonstationary signals;random under-sampling;time-frequency representation}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925537.pdf}, }

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