  We propose a novel system identification technique, based on a least-mean
square algorithm, allowing for the estimation of a linear channel by using an
unknown-response measurement channel. The key of the technique is a memoryless
nonlinear function working as uncoupling block between the estimated and
observation channels, conforming a Wiener-Hammerstein scheme. We prove that
this estimation, only differing from the actual channel response by a scaling
factor and a temporal shift, does not depend on the observation channel
bandwidth. As a consequence, this technique enables the usage of low-cost
measurement devices as feedback channel. We present numerical examples of the
method, supporting the proposal and displaying excellent results.
