Journal of Approximation Theory
Interpolation, spectrum analysis, error-control coding, and fault-tolerant computing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
On the accuracy and resolution of powersum-based sampling methods
IEEE Transactions on Signal Processing
Blind multiband signal reconstruction: compressed sensing for analog signals
IEEE Transactions on Signal Processing
Compressed sensing of analog signals in shift-invariant spaces
IEEE Transactions on Signal Processing
Estimating Signals With Finite Rate of Innovation From Noisy Samples: A Stochastic Algorithm
IEEE Transactions on Signal Processing - Part II
Reconstructing polygons from moments with connections to arrayprocessing
IEEE Transactions on Signal Processing
Cardinal exponential splines: part I - theory and filtering algorithms
IEEE Transactions on Signal Processing
Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
IEEE Transactions on Signal Processing
Shape from moments - an estimation theory perspective
IEEE Transactions on Signal Processing
Sampling and reconstruction of signals with finite rate of innovation in the presence of noise
IEEE Transactions on Signal Processing - Part I
Sampling signals with finite rate of innovation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Sampling and exact reconstruction of bandlimited signals with additive shot noise
IEEE Transactions on Information Theory
Estimating multiple frequency-hopping signal parameters via sparse linear regression
IEEE Transactions on Signal Processing
Representation of sparse Legendre expansions
Journal of Symbolic Computation
Hi-index | 35.69 |
We consider the problem of sampling piecewise sinusoidal signals. Classical sampling theory does not enable perfect reconstruction of such signals since they are not band-limited. However, they can be characterized by a finite number of parameters, namely, the frequency, amplitude, and phase of the sinusoids and the location of the discontinuities. In this paper, we showthat under certain hypotheses on the sampling kernel, it is possible to perfectly recover the parameters that define the piecewise sinusoidal signal from its sampled version. In particular, we show that, at least theoretically, it is possible to recover piecewise sine waves with arbitrarily high frequencies and arbitrarily close switching points. Extensions of the method are also presented such as the recovery of combinations of piecewise sine waves and polynomials. Finally, we study the effect of noise and present a robust reconstruction algorithm that is stable down to SNR levels of 7 [dB].