This paper is devoted to the analysis of a "semi-blind" estimation framewor
k in which the standard least-squares estimator (based on a known training
sequence) is enhanced by using the statistical structure of the observation
s. More specifically, we consider the case of a general time-division multi
ple access (TDMA) frame-based receiver equipped with multiple sensor and re
strict our attention to second-order based subspace methods that are suitab
le for most standard communication applications due to their moderate compu
tational cost. The semi-blind channel estimator is obtained as a regularize
d least-squares solution where a blind subspace criterion plays the role of
the regularization constraint. The main contribution of the paper consists
of showing by asymptotic analysis hen; to optimally tune the balance betwe
en the blind criterion and the least-squares fit, depending on the design p
arameters of the system. Simulations show that the proposed solutions are r
obust and significantly improve the efficiency of the equalization.