New Insights into the Kalman Filter Beamformer: Applications to Speech and Robustness

Dani Cherkassky, Sharon Gannot

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Statistically optimal spatial processors (also referred to as data-dependent beamformers) are widely-used spatial focusing techniques for desired source extraction. The Kalman filterbased beamformer (KFB) [1] is a recursive Bayesian method for implementing the beamformer. This letter provides new insights into the KFB. Specifically, we adopt the KFB framework to the task of speech extraction. We formalize the KFB with a set of linear constraints and present its equivalence to the linearly constrained minimum power (LCMP) beamformer. We further show that the optimal output power, required for implementing the KFB, is merely controlling the white noise gain (WNG) of the beamformer. We also show, that in static scenarios, the adaptation rule of the KFB reduces to the simpler affine projection algorithm (APA). The analytically derived results are verified and exemplified by a simulation study.

Original languageEnglish
Article number7386586
Pages (from-to)376-380
Number of pages5
JournalIEEE Signal Processing Letters
Issue number3
StatePublished - Mar 2016

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Kalman filter
  • LCMP beamformer
  • adaptive beamformer
  • affine projection algorithm
  • microphone arrays
  • speech enhancement
  • speech extraction


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