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—The electromagnetic induction response of a target can be accurately modeled by a sum of real exponentials. However, it is difficult to obtain the model parameters from measurements when the number of exponentials in the sum is unknown or the terms are strongly correlated. Traditionally, the time constants and residues are estimated by nonlinear iterative… (More)

It is difficult to robustly estimate the parameters of an additive exponential model from a small number of frequency-domain measurements, especially when the model order is unknown and the parameters must be constrained to be real. Recent work in sparse sampling and sparse reconstruction casts this problem as a linear dictionary selection problem by… (More)

The EMI response of a target can be accurately modeled by a sum of real exponentials. However, it is difficult to obtain the model parameters from measurements when the number of exponentials in the sum is unknown. In this paper, a constrained linear method for estimating the parameters is formulated by enumerating the relaxation parameter space and… (More)

An iterative reweighted algorithm is proposed for the recovery of jointly sparse vectors from multiple-measurement vectors (MMV). The proposed MMV algorithm is an extension of the iterative reweighted 1 algorithm for single measurement problems. The proposed algorithm (M-IRL1) is demonstrated to outperform non-reweighted MMV algorithms under noiseless… (More)

It is difficult to robustly estimate the parameters of an additive exponential model from a small number of frequency-domain measurements, especially when the model order is unknown and the parameters must be constrained to be real. Recent work in sparse sampling and sparse reconstruction casts this problem as a linear dictionary selection problem by… (More)