{"_id":"DRqp7FvjsWeDsC6bv","bibbaseid":"arablouei-doanay-adali-unbiasedrlsidentificationoferrorsinvariablesmodelsinthepresenceofcorrelatednoise-2014","authorIDs":[],"author_short":["Arablouei, R.","Doğançay, K.","Adali, T."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["R."],"propositions":[],"lastnames":["Arablouei"],"suffixes":[]},{"firstnames":["K."],"propositions":[],"lastnames":["Doğançay"],"suffixes":[]},{"firstnames":["T."],"propositions":[],"lastnames":["Adali"],"suffixes":[]}],"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","bibtex":"@InProceedings{6952031,\n author = {R. Arablouei and K. Doğançay and T. 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. However, the URLS algorithm has appreciably lower computational complexity as well as improved numerical stability compared with the BCRLS algorithm.},\n 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},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569909475.pdf},\n}\n\n","author_short":["Arablouei, R.","Doğançay, K.","Adali, T."],"key":"6952031","id":"6952031","bibbaseid":"arablouei-doanay-adali-unbiasedrlsidentificationoferrorsinvariablesmodelsinthepresenceofcorrelatednoise-2014","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569909475.pdf"},"keyword":["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"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.587Z","downloads":0,"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"],"search_terms":["unbiased","rls","identification","errors","variables","models","presence","correlated","noise","arablouei","doğançay","adali"],"title":"Unbiased RLS identification of errors-in-variables models in the presence of correlated noise","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}