Quantization effects and stabilization of the fast-Kalman algorithm

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dc.contributor.author Papaodysseus, C en
dc.contributor.author Alexiou, C en
dc.contributor.author Roussopoulos, G en
dc.contributor.author Panagopoulos, A en
dc.date.accessioned 2014-03-01T01:16:59Z
dc.date.available 2014-03-01T01:16:59Z
dc.date.issued 2001 en
dc.identifier.issn 11108657 en
dc.identifier.uri http://hdl.handle.net/123456789/14304
dc.subject Adaptive algorithms en
dc.subject Finite-precision error in RLS algorithms en
dc.subject Kalman filtering en
dc.subject Quantization error in fast-Kalman algorithm en
dc.subject Recursive least squares filtering en
dc.subject.other Acoustic noise en
dc.subject.other Adaptive algorithms en
dc.subject.other Kalman filtering en
dc.subject.other Recursive functions en
dc.subject.other Finite-precision errors en
dc.subject.other Recursive least square (RLS) schemes en
dc.subject.other Adaptive filtering en
dc.title Quantization effects and stabilization of the fast-Kalman algorithm en
heal.type journalArticle en
heal.identifier.primary 10.1155/S1110865701000014 en
heal.identifier.secondary http://dx.doi.org/10.1155/S1110865701000014 en
heal.publicationDate 2001 en
heal.abstract The exact and actual cause of the failure of the fast-Kalman algorithm due to the generation and propagation of finite-precision or quantization error is presented. It is demonstrated that out of all the formulas that constitute this fast Recursive Least Squares (RLS) scheme only three generate an amount of finite-precision error that consistently propagates in the subsequent iterations and eventually makes the algorithm fail after a certain number of recursions. Moreover, it is shown that there is a very limited number of specific formulas that transmit the generated finite-precision error, while there is another class of formulas that lift or ""relax"" this error. In addition, a number of general propositions is presented that allow for the calculation of the exact number of erroneous digits with which the various quantifies of the fast-Kalman scheme are computed, including the filter coefficients. On the basis of the previous analysis a method of stabilization of the fast-Kalman algorithm is developed and is presented here, a method that allows for the fast-Kalman algorithm to follow very difficult signals such as music, speech, environmental noise, and other nonstationary ones. Finally, a general methodology is pointed out, that allows for the development of new algorithms which, intrinsically, suffer far less of finite-precision problems. en
heal.journalName Eurasip Journal on Applied Signal Processing en
dc.identifier.doi 10.1155/S1110865701000014 en
dc.identifier.volume 2001 en
dc.identifier.issue 3 en
dc.identifier.spage 169 en
dc.identifier.epage 180 en

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