Spectral Compressed Sensing via Projected Gradient Descent
نویسندگان
چکیده
منابع مشابه
Spectral Compressed Sensing via Projected Gradient Descent
Let x ∈ C be a spectrally sparse signal consisting of r complex sinusoids with or without damping. We consider the spectral compressed sensing problem, which is about reconstructing x from its partial revealed entries. By utilizing the low rank structure of the Hankel matrix corresponding to x, we develop a computationally efficient algorithm for this problem. The algorithm starts from an initi...
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We extend PGD and its recovery guarantee [1] from one-dimensional spectrally sparse signal recovery to the multi-dimensional case. Assume the underlying multi-dimensional spectrally sparse signal is of model order r and total dimension N . We show that O(r log(N)) measurements are sufficient for PGD to achieve successful recovery with high probability provided the underlying signal satisfies so...
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This paper considers reconstructing a spectrally sparse signal from a small number of randomly observed time-domain samples. The signal of interest is a linear combination of complex sinusoids at R distinct frequencies. The frequencies can assume any continuous values in the normalized frequency domain [0, 1). After converting the spectrally sparse signal recovery into a low rank structured mat...
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The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of interest with ambient dimension n is assumed to be a mixture of r complex multi-dimensional sinusoids, while the underlying frequencies can assume any value in the unit disk. Conventional compressed sensing paradigms suffer from the basis mismatch issue ...
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ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2018
ISSN: 1052-6234,1095-7189
DOI: 10.1137/17m1141394