%0 Conference Paper %1 Penarrocha2010 %A Pe\ narrocha, I. %A Sanchis, R. %A Penarrocha, I. %B 49th IEEE Conference on Decision and Control (CDC) %D 2010 %I IEEE %K Algorithm Identification,Randomly Kalman Recursive algorithm,parameter algorithms,Pseudo-Linear control data,networked estimation estimation,Parameter estimation,Prediction estimation,recursive extended filter,Kalman filter,linear filters,Least filters,adaptive identification,parameter identification,regression initialization,Convergence,Covariance matrix,Estimation,Kalman measurement,Output missing outputs,adaptive plant,missing squares,Noise systems,parameter vector vector,recursive %P 1165--1170 %R 10.1109/CDC.2010.5717484 %T Adaptive extended Kalman filter for recursive identification under missing data %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5717484 %X In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise measurement. The identification is carried out estimating the missing outputs in order to construct the regression vector needed by the parameter estimation algorithm and using the available output information not only to update the estimated parameter vector, but also to update the regression vector in order to fasten the convergence of the algorithm. The problem is addressed with an adaptive extended Kalman filter that estimates and correct both the parameters and the regression vector, allowing to improve the convergence speed of the algorithm with respect to other existing ones on the literature as it is shown with several examples. %@ 978-1-4244-7745-6

@inproceedings{Penarrocha2010, abstract = {In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise measurement. The identification is carried out estimating the missing outputs in order to construct the regression vector needed by the parameter estimation algorithm and using the available output information not only to update the estimated parameter vector, but also to update the regression vector in order to fasten the convergence of the algorithm. The problem is addressed with an adaptive extended Kalman filter that estimates and correct both the parameters and the regression vector, allowing to improve the convergence speed of the algorithm with respect to other existing ones on the literature as it is shown with several examples.}, added-at = {2013-03-23T10:56:50.000+0100}, author = {Pe\ {n}arrocha, I. and Sanchis, R. and Penarrocha, I.}, biburl = {https://www.bibsonomy.org/bibtex/2d804f9b8205c750baf8a5bf8b34e1fd2/ipenarro}, booktitle = {49th IEEE Conference on Decision and Control (CDC)}, doi = {10.1109/CDC.2010.5717484}, file = {:X$\backslash$:/PDF files/05717484.pdf:pdf}, interhash = {84e3c41281c8cd81e64da88709fec5d3}, intrahash = {d804f9b8205c750baf8a5bf8b34e1fd2}, isbn = {978-1-4244-7745-6}, keywords = {Algorithm Identification,Randomly Kalman Recursive algorithm,parameter algorithms,Pseudo-Linear control data,networked estimation estimation,Parameter estimation,Prediction estimation,recursive extended filter,Kalman filter,linear filters,Least filters,adaptive identification,parameter identification,regression initialization,Convergence,Covariance matrix,Estimation,Kalman measurement,Output missing outputs,adaptive plant,missing squares,Noise systems,parameter vector vector,recursive}, language = {English}, mendeley-tags = {Algorithm initialization,Convergence,Covariance matrix,Estimation,Kalman filter,Kalman filters,Least squares,Noise measurement,Output estimation,Parameter estimation,Prediction algorithms,Pseudo-Linear Recursive Identification,Randomly missing outputs,adaptive Kalman filters,adaptive extended Kalman filter,linear plant,missing data,networked control systems,parameter estimation algorithm,parameter identification,parameter vector,recursive estimation,recursive identification,regression vector}, month = dec, pages = {1165--1170}, publisher = {IEEE}, timestamp = {2013-03-23T10:56:52.000+0100}, title = {{Adaptive extended Kalman filter for recursive identification under missing data}}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5717484}, year = 2010 }