  Blind identification is popular for modeling a system without the input
information, such as in the research areas of structural health monitoring and
audio signal processing. Existing blind identification methods have both
advantages and disadvantages, in this paper, we briefly outline current methods
and propose a novel blind identification method for identifying state-space
models in physical coordinates. The idea behind this proposed method is first
to regard the collected input data of a state-space model as a part of a
periodic signal sequence, and then transform the state-space model with input
and output into a model without input by augmenting the state-space model with
the input model (which is a periodic signal model), and afterwards use merely
the output information to identify a state-space model up to a similarity
transformation, and finally derive the state-space model in physical
coordinates by using a unique similarity transformation. With the above idea,
physical parameters and modal parameters of a state-space system can be
obtained. Both numerical and practical examples were used to validate the
proposed method. The result showed the effectiveness of the novel blind
identification method.
