  We describe a method for signal parameter estimation using the signed
cumulative distribution transform (SCDT), a recently introduced signal
representation tool based on optimal transport theory. The method builds upon
signal estimation using the cumulative distribution transform (CDT) originally
introduced for positive distributions. Specifically, we show that
Wasserstein-type distance minimization can be performed simply using linear
least squares techniques in SCDT space for arbitrary signal classes, thus
providing a global minimizer for the estimation problem even when the
underlying signal is a nonlinear function of the unknown parameters.
Comparisons to current signal estimation methods using $L_p$ minimization shows
the advantage of the method.
