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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 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)
Recent developments in the estimation of the discrete spectrum of relaxation frequencies (DSRFs) has opened doors to more robust subsurface target discrimination using electromagnetic induction measurements. In particular, a nonnegative least squares DSRF (NNLSQ-DSRF) estimation method has been shown to be robust and free from parameter tuning. In this(More)
The landmine crisis remains today as mines continue to maim or kill civilians everyday worldwide. The International Campaign to Ban Landmines reported that in the year of 2007, mines and explosive remnants of war caused 5426 casualties worldwide, of which 67% are civilians [1]. Much effort and research have been invested in remediating landmines with one of(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)
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)