  This paper is devoted to the performance analysis of the detectors proposed
in the companion paper where a comprehensive design framework is presented for
the adaptive detection of subspace signals. The framework addresses four
variations on subspace detection: the subspace may be known or known only by
its dimension; consecutive visits to the subspace may be unconstrained or they
may be constrained by a prior probability distribution. In this paper, Monte
Carlo simulations are used to compare the generalized likelihood ratio (GLR)
detectors derived in [1] with estimate-and-plug (EP) approximations of the GLR
detectors. Remarkably, the EP approximations appear here for the first time (at
least to the best of the authors' knowledge). The numerical examples indicate
that GLR detectors are effective for the detection of partially-known signals
affected by inherent uncertainties due to the system or the operating
environment. In particular, if the signal subspace is known, GLR detectors tend
to outperform EP detectors. If, instead, the signal subspace is known only by
its dimension, the performance of GLR and EP detectors is very similar.
