We propose a joint sparse signal recovery approach to coherent spectral analysis of irregularly sampled signals. These signals share the same frequencies, and are sampled asynchronously. Two types of solution procedures are considered. First one is a convex optimization approach, which optimizes a mixed ℓ ₂,₁ -norm. The other method minimizes an approximation of ℓ ₂,<sub>0</sub> -norm and the resulting algorithm can be implemented using a few FFTs and IFFTs. We demonstrate the effectiveness of the sparse recovery approach using simulation experiments. In particular, the ℓ ₂,<sub>0</sub> approximation approach is very fast. In addition, it offers increased resolution, improved robustness to noise, and works well with limited number of data s...
Existing approaches to Compressive Sensing (CS) of sparse spectrum has thus far assumed models conta...
The frequency estimation of a complex sine wave in noise is one of the main research contents of s...
We propose a fast algorithm to reconstruct spectrally sparse signals from a small number of randomly...
Inferring the fine scale properties of a signal from its coarse measurements is a common signal proc...
Frequency recovery/estimation from samples of superimposed sinusoidal signals is a classical problem...
It is possible to reconstruct a signal from cyclic nonuniform samples and thus take advantage of a l...
In this paper we consider the problem of recovering tempo-rally smooth or correlated sparse signals ...
We address the problem of finding a set of sparse signals that have nonzero coefficients in the same...
Accurate measurement of a multisine waveform is a classic spectral analysis problem. Algorithms base...
In this paper we consider the problem of recovering tempo-rally smooth or correlated sparse signals ...
While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit ...
Signal recovery from the amplitudes of the Fourier transform, or equivalently from the autocorrelati...
Existing approaches to Compressive Sensing (CS) of sparse spectrum has thus far assumed models conta...
Abstract—This paper addresses the problem of expressing a signal as a sum of frequency components (s...
Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most ...
Existing approaches to Compressive Sensing (CS) of sparse spectrum has thus far assumed models conta...
The frequency estimation of a complex sine wave in noise is one of the main research contents of s...
We propose a fast algorithm to reconstruct spectrally sparse signals from a small number of randomly...
Inferring the fine scale properties of a signal from its coarse measurements is a common signal proc...
Frequency recovery/estimation from samples of superimposed sinusoidal signals is a classical problem...
It is possible to reconstruct a signal from cyclic nonuniform samples and thus take advantage of a l...
In this paper we consider the problem of recovering tempo-rally smooth or correlated sparse signals ...
We address the problem of finding a set of sparse signals that have nonzero coefficients in the same...
Accurate measurement of a multisine waveform is a classic spectral analysis problem. Algorithms base...
In this paper we consider the problem of recovering tempo-rally smooth or correlated sparse signals ...
While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit ...
Signal recovery from the amplitudes of the Fourier transform, or equivalently from the autocorrelati...
Existing approaches to Compressive Sensing (CS) of sparse spectrum has thus far assumed models conta...
Abstract—This paper addresses the problem of expressing a signal as a sum of frequency components (s...
Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most ...
Existing approaches to Compressive Sensing (CS) of sparse spectrum has thus far assumed models conta...
The frequency estimation of a complex sine wave in noise is one of the main research contents of s...
We propose a fast algorithm to reconstruct spectrally sparse signals from a small number of randomly...