  The identification of multivariable state space models in innovation form is
solved in a subspace identification framework using convex nuclear norm
optimization. The convex optimization approach allows to include constraints on
the unknown matrices in the data-equation characterizing subspace
identification methods, such as the lower triangular block-Toeplitz of
weighting matrices constructed from the Markov parameters of the unknown
observer. The classical use of instrumental variables to remove the influence
of the innovation term on the data equation in subspace identification is
avoided. The avoidance of the instrumental variable projection step has the
potential to improve the accuracy of the estimated model predictions,
especially for short data length sequences. This is illustrated using a data
set from the DaSIy library. An efficient implementation in the framework of the
Alternating Direction Method of Multipliers (ADMM) is presented that is used in
the validation study.
