Circuits, Systems, and Signal Processing
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Statistical analysis of TLS-based Prony techniques
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Spikes: exploring the neural code
Spikes: exploring the neural code
Statistical resolution limits and the complexified Crame´r-Rao bound
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
Nonmatrix Cramer-Rao bound expressions for high-resolutionfrequency estimators
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
Analog-to-digital converter survey and analysis
IEEE Journal on Selected Areas in Communications
Ultrawide bandwidth signals as shot noise: a unifying approach
IEEE Journal on Selected Areas in Communications - Part 1
Sampling piecewise sinusoidal signals with finite rate of innovation methods
IEEE Transactions on Signal Processing
Hi-index | 35.69 |
Recently, several sampling methods suitable for signals that are sums of Diracs have been proposed. Though they are implemented through different acquisition architectures, these methods all rely on estimating the parameters of a powersum series. We derive Cramér-Rao lower bounds (CRBs) for estimation of the powersum poles, which translate to the Dirac positions. We then demonstrate the efficacy of simple algorithms due to Prony and Cornell for low-order powersums and low oversampling relative to the rate of innovation. The simulated performance illustrates the possibility of superresolution reconstruction and robustness to correlation in the powersum sample noise.