  We develop an approach to subspace system identification using multiple data
records and present a simple rank-based test for the adequacy of these data for
fitting the unique linear, noise-free, dynamic model of prescribed
state-vector, input-vector and output-vector dimensions. The approach is
motivated by the prospect of sorting through archives of operational data and
extracting a sequence of not-necessarily-contiguous data records individually
insufficient for providing identifiability but collectively making this
possible. The test of identifiability then becomes the sorting criterion for
accepting or rejecting new data records. En passant, the familiar Hankel
structure of the data matrices of subspace system identification is
reinterpreted and revised.
