  In this paper, we present a realization and an identification algorithm for
stochastic Linear Parameter-Varying State-Space Affine (LPV-SSA)
representations. The proposed realization algorithm combines the deterministic
LPV input output to LPV state-space realization scheme based on correlation
analysis with a stochastic covariance realization algorithm. Based on this
realization algorithm, a computationally efficient and statistically consistent
identification algorithm is proposed to estimate the LPV model matrices, which
are computed from the empirical covariance matrices of outputs, inputs and
scheduling signal observations. The effectiveness of the proposed algorithm is
shown via a numerical case study.
