A new robust method lifts corrupted dynamical sampling to low-rank Hankel recovery and Prony estimation to recover spectra accurately despite time-sparse outliers.
Convolutional dynam- ical sampling and some new results
2 Pith papers cite this work. Polarity classification is still indexing.
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Necessary and sufficient conditions are given for source term recovery in finite and infinite iterations from non-uniform dynamical samples arising from spectral pairs in separable Hilbert spaces.
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Robust Spectral Recovery for Dynamical Sampling
A new robust method lifts corrupted dynamical sampling to low-rank Hankel recovery and Prony estimation to recover spectra accurately despite time-sparse outliers.
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Frames for source recovery from non-uniform dynamical samples
Necessary and sufficient conditions are given for source term recovery in finite and infinite iterations from non-uniform dynamical samples arising from spectral pairs in separable Hilbert spaces.