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This paper deals with optimal (minimal variance) filtering in an errors-in-variables framework. Differently from many other contexts, errors-in-variables models treat all variables in a symmetric way (no partition of the variables into inputs and outputs is required) and assume additive noise on all the variables. The filtering technique described in this paper can be easily implemented in a recursive...
This paper introduces a new algorithm for optimal filtering of data generated by errors-in-variables processes and compares its efficiency with that of two previous algorithms (Optimal errors-in-variables filtering, to appear in Automatica). It is shown that the new approach proposed here, based on the Cholesky decomposition of a matrix, is characterized by a high level of efficiency, superior to...
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