  The problem of parameter estimation from a standard vector linear regression
equation in the absence of sufficient excitation in the regressor is addressed.
The first step to solve the problem consists in transforming this equation into
a set of scalar ones using the well-known dynamic regressor extension and
mixing technique. Then a novel procedure to generate new scalar exciting
regressors is proposed.} The superior performance of a classical gradient
estimator using this new regressor, instead of the original one, is illustrated
with comprehensive simulations.
