**Reference:**

Z. Hidayat,
A. Núñez,
R. Babuska, and
B. De Schutter,
"Identification of distributed-parameter systems with missing data,"
*Proceedings of the 2012 IEEE International Conference on Control
Applications*, Dubrovnik, Croatia, pp. 1014-1019, Oct. 2012.

**Abstract:**

In this paper we address the identification of linear
distributed-parameter systems with missing data. This setting is
relevant in, for instance, sensor networks, where data are frequently
lost due to transmission errors. We consider an identification problem
where the only information available about the system are the
input-output measurements from a set of sensors placed at known fixed
locations in the distributed-parameter system. The model is
represented as a set of coupled multi-input, single-output
autoregressive with exogenous input (ARX) submodels. Total
least-squares estimation is employed to obtain an unbiased parameter
estimate in the presence of sensor noise. The missing samples are
reconstructed with the help of an iterative algorithm. To approximate
the value of the variables of interest in locations with no sensors,
we use cubic B-splines to preserve the continuity of the first-order
and second-order spatial derivatives. The method is applied to a
simulated one-dimensional heat-conduction process.

Corresponding technical report: pdf file (186 KB)

@inproceedings{HidDeS:12-023,

author={Z. Hidayat and A. N{\'{u}}{\~{n}}ez and R. Babu{\v{s}}ka and B. {D}e Schutter},

title={Identification of distributed-parameter systems with missing data},

booktitle={Proceedings of the 2012 IEEE International Conference on Control Applications},

address={Dubrovnik, Croatia},

pages={1014--1019},

month=oct,

year={2012}

}

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