Abstract
For the blind identification problem, the concerned system model is usually represented as a transfer function vector/matrix, and the identification task aims to recover the transfer function vector/matrix as well as the unknown input signal using only the output observations. So far, there are rarely any research on the blind identification of state-space models, since a state-space model will introduce unknown system states and pose identification challenges. Although the blind identification of multi-channel state-space models has been investigated in Chap. 8, it relies on the special SIMO structure of the system model. As an extension, the blind identification of structured state-space model will be investigated in this chapter.
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Yu, C., Xie, L., Verhaegen, M., Chen, J. (2022). Blind Identification of Structured State-Space Models. In: Blind Identification of Structured Dynamic Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7574-4_9
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DOI: https://doi.org/10.1007/978-981-16-7574-4_9
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