  A fundamental pursuit of microwave metrology is the determination of the
characteristic impedance profile of microwave systems. Among other methods,
this can be practically achieved by means of time-domain reflectometry (TDR)
that measures the reflections from a device due to an applied stimulus.
Conventional TDR allows for the measurement of systems comprising a single
impedance. However, real systems typically feature impedance variations that
obscure the determination of all impedances subsequent to the first one. This
problem has been studied previously and is generally known as scattering
inversion or, in the context of microwave metrology, time-domain "peeling". In
this article, we demonstrate the implementation of a space-time efficient
peeling algorithm that corrects for the effect of prior impedance mismatch in a
nonuniform lossless transmission line, regardless of the nature of the
stimulus. We generalize TDR measurement analysis by introducing two tools: A
stochastic machine learning clustering tool and an arbitrary lossy transmission
line modeling tool. The former mitigates many of the imperfections typically
plaguing TDR measurements (except for dispersion) and allows for an efficient
processing of large datasets; the latter allows for a complete transmission
line characterization including both conductor and dielectric loss.
