Statistically optimal spatial processors (also referred to as data-dependent beamformers) are widely-used spatial focus- ing techniques for desired source extraction. The Kalman filter- based 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 con- strained 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 beam- former. We also show, that in static scenarios, the adaptation rule of the KFB reduces to the simpler affine projection algo- rithm (APA). The analytically derived results are verified and exemplified by a simulation study.
['Dani Cherkassky', 'Sharon Gannot']
New Insights into the Kalman Filter Beamformer: Applications to Speech and Robustness