@InProceedings{6952031,
  author = {R. Arablouei and K. Doğançay and T. Adali},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Unbiased RLS identification of errors-in-variables models in the presence of correlated noise},
  year = {2014},
  pages = {261-265},
  abstract = {We propose an unbiased recursive-least-squares(RLS)-type algorithm for errors-in-variables system identification when the input noise is colored and correlated with the output noise. To derive the proposed algorithm, which we call unbiased RLS (URLS), we formulate an exponentially-weighted least-squares problem that yields an unbiased estimate. Then, we solve the associated normal equations utilizing the dichotomous coordinate-descent iterations. Simulation results show that the estimation performance of the proposed URLS algorithm is similar to that of a previously proposed bias-compensated RLS (BCRLS) algorithm. However, the URLS algorithm has appreciably lower computational complexity as well as improved numerical stability compared with the BCRLS algorithm.},
  keywords = {least squares approximations;recursive estimation;unbiaseD RLS identification;correlated noise;recursive-least-squares-type algorithm;errors-in-variable system identification;URLS algorithm;exponentially-weighted least-squares problem;dichotomous coordinate-descent iterations;bias-compensated RLS algorithm;BCRLS algorithm;Noise;Uniform resource locators;Abstracts;Indexes;Vectors;Complexity theory;Field programmable gate arrays;Adaptive estimation;dichotomous coordinate-descent algorithm;errors-in-variables modeling;recursive least-squares;system identification},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569909475.pdf},
}

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To derive the proposed algorithm, which we call unbiased RLS (URLS), we formulate an exponentially-weighted least-squares problem that yields an unbiased estimate. Then, we solve the associated normal equations utilizing the dichotomous coordinate-descent iterations. Simulation results show that the estimation performance of the proposed URLS algorithm is similar to that of a previously proposed bias-compensated RLS (BCRLS) algorithm. 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Adali},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Unbiased RLS identification of errors-in-variables models in the presence of correlated noise},\n year = {2014},\n pages = {261-265},\n abstract = {We propose an unbiased recursive-least-squares(RLS)-type algorithm for errors-in-variables system identification when the input noise is colored and correlated with the output noise. To derive the proposed algorithm, which we call unbiased RLS (URLS), we formulate an exponentially-weighted least-squares problem that yields an unbiased estimate. Then, we solve the associated normal equations utilizing the dichotomous coordinate-descent iterations. Simulation results show that the estimation performance of the proposed URLS algorithm is similar to that of a previously proposed bias-compensated RLS (BCRLS) algorithm. 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