  Blind calibration of sensors arrays (without using calibration signals) is an
important, yet challenging problem in array processing. While many methods have
been proposed for "classical" array structures, such as uniform linear arrays,
not as many are found in the context of the more "modern" sparse arrays. In
this paper, we present a novel blind calibration method for $2$-level nested
arrays. Specifically, and despite recent contradicting claims in the
literature, we show that the Least-Squares (LS) approach can in fact be used
for this purpose with such arrays. Moreover, the LS approach gives rise to
optimally-weighted LS joint estimation of the sensors' gains and phases
offsets, which leads to more accurate calibration, and in turn, to higher
accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our
method, which can be extended to $K$-level arrays ($K>2$), is superior to the
current state of the art both in terms of accuracy and computational
efficiency, as we demonstrate in simulation.
