  Linear, time-varying (LTV) systems composed of time shifts, frequency shifts,
and complex amplitude scalings are operators that act on continuous
finite-energy waveforms. This paper presents a novel, resource-efficient method
for identifying the parametric description of such systems, i.e., the time
shifts, frequency shifts, and scalings, from the sampled response to linear
frequency modulated (LFM) waveforms, with emphasis on the application to radar
processing. If the LTV operator is probed with a sufficiently diverse set of
LFM waveforms, then the system can be identified with high accuracy. In the
case of noiseless measurements, the identification is perfect, while in the
case of noisy measurements, the accuracy is inversely proportional to the noise
level. The use of parametric estimation techniques with recently proposed
denoising algorithms allows the estimation of the parameters with high
accuracy.
