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A general class of recursive minimum variance distortionless response estimators

Abstract : In deterministic parameters estimation, it is common place to design a minimum variance distortionless response estimator (MVDRE) instead of a maximum likelihood estimator to tackle the problem of identifying the components of observations formed from a linear superposition of individual signals to noisy data. When several observations are available and the individual signals are allowed to perform a random walk between observations, one obtains the general class of linear discrete state-space models. This paper introduces a novel recursive formulation of the MVDREs of individual signals compatible with recursive estimation.
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Submitted on : Thursday, January 28, 2021 - 11:41:41 AM
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Jérôme Galy, Eric Chaumette, Francois Vincent. A general class of recursive minimum variance distortionless response estimators. 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017), Dec 2017, Curacao, Netherlands. pp.1-5, ⟨10.1109/CAMSAP.2017.8313131⟩. ⟨lirmm-03123911⟩



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